Overview

Dataset statistics

Number of variables46
Number of observations11507
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.0 MiB
Average record size in memory368.0 B

Variable types

Numeric27
Text18
Categorical1

Alerts

monster_number is highly overall correlated with monster_player_ratio and 4 other fieldsHigh correlation
monster_player_ratio is highly overall correlated with monster_number and 1 other fieldsHigh correlation
monster_total_level is highly overall correlated with party_total_level and 1 other fieldsHigh correlation
party_level1_spellslots is highly overall correlated with party_level2_spellslots and 12 other fieldsHigh correlation
party_level2_spellslots is highly overall correlated with party_level1_spellslots and 11 other fieldsHigh correlation
party_level3_spellslots is highly overall correlated with party_level1_spellslots and 6 other fieldsHigh correlation
party_level4_spellslots is highly overall correlated with party_level2_spellslots and 5 other fieldsHigh correlation
party_level5_spellslots is highly overall correlated with party_level3_spellslots and 4 other fieldsHigh correlation
party_level6_spellslots is highly overall correlated with party_level4_spellslots and 4 other fieldsHigh correlation
party_level7_spellslots is highly overall correlated with party_level4_spellslots and 4 other fieldsHigh correlation
party_level8_spellslots is highly overall correlated with party_level5_spellslots and 3 other fieldsHigh correlation
party_level9_spellslots is highly overall correlated with party_level6_spellslots and 2 other fieldsHigh correlation
party_size is highly overall correlated with monster_number and 12 other fieldsHigh correlation
party_total_ac is highly overall correlated with party_level1_spellslots and 12 other fieldsHigh correlation
party_total_charisma is highly overall correlated with party_level1_spellslots and 11 other fieldsHigh correlation
party_total_constitution is highly overall correlated with monster_number and 13 other fieldsHigh correlation
party_total_dexterity is highly overall correlated with party_level1_spellslots and 10 other fieldsHigh correlation
party_total_intelligence is highly overall correlated with monster_number and 12 other fieldsHigh correlation
party_total_level is highly overall correlated with monster_total_level and 15 other fieldsHigh correlation
party_total_postcombat_hp is highly overall correlated with party_total_ac and 4 other fieldsHigh correlation
party_total_precombat_hp is highly overall correlated with monster_total_level and 14 other fieldsHigh correlation
party_total_prof_bonus is highly overall correlated with party_level1_spellslots and 13 other fieldsHigh correlation
party_total_strength is highly overall correlated with party_level1_spellslots and 10 other fieldsHigh correlation
party_total_wisdom is highly overall correlated with party_level1_spellslots and 11 other fieldsHigh correlation
player_monster_ratio is highly overall correlated with monster_number and 1 other fieldsHigh correlation
party_level9_spellslots is highly imbalanced (87.8%)Imbalance
Unnamed: 0 has unique valuesUnique
combat_id has unique valuesUnique
start_time has unique valuesUnique
monsters_info has unique valuesUnique
monster_total_level has 321 (2.8%) zerosZeros
party_level1_spellslots has 2780 (24.2%) zerosZeros
party_level2_spellslots has 3943 (34.3%) zerosZeros
party_level3_spellslots has 6082 (52.9%) zerosZeros
party_level4_spellslots has 7990 (69.4%) zerosZeros
party_level5_spellslots has 8984 (78.1%) zerosZeros
party_level6_spellslots has 9985 (86.8%) zerosZeros
party_level7_spellslots has 10320 (89.7%) zerosZeros
party_level8_spellslots has 10624 (92.3%) zerosZeros
party_total_postcombat_hp has 345 (3.0%) zerosZeros
party_total_hpratio has 345 (3.0%) zerosZeros

Reproduction

Analysis started2024-04-03 18:34:00.977121
Analysis finished2024-04-03 18:35:01.120770
Duration1 minute and 0.14 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ)

UNIQUE 

Distinct11507
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12349.383
Minimum1
Maximum24746
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size90.0 KiB
2024-04-03T14:35:01.189602image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1178.2
Q16087.5
median12341
Q318559.5
95-th percentile23540.7
Maximum24746
Range24745
Interquartile range (IQR)12472

Descriptive statistics

Standard deviation7168.6965
Coefficient of variation (CV)0.58049026
Kurtosis-1.205207
Mean12349.383
Median Absolute Deviation (MAD)6237
Skewness0.0030453317
Sum1.4210435 × 108
Variance51390210
MonotonicityStrictly increasing
2024-04-03T14:35:01.291080image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
16592 1
 
< 0.1%
16549 1
 
< 0.1%
16551 1
 
< 0.1%
16554 1
 
< 0.1%
16557 1
 
< 0.1%
16558 1
 
< 0.1%
16559 1
 
< 0.1%
16562 1
 
< 0.1%
16565 1
 
< 0.1%
Other values (11497) 11497
99.9%
ValueCountFrequency (%)
1 1
< 0.1%
7 1
< 0.1%
10 1
< 0.1%
12 1
< 0.1%
13 1
< 0.1%
15 1
< 0.1%
18 1
< 0.1%
19 1
< 0.1%
27 1
< 0.1%
29 1
< 0.1%
ValueCountFrequency (%)
24746 1
< 0.1%
24744 1
< 0.1%
24742 1
< 0.1%
24741 1
< 0.1%
24739 1
< 0.1%
24732 1
< 0.1%
24731 1
< 0.1%
24730 1
< 0.1%
24728 1
< 0.1%
24725 1
< 0.1%

combat_id
Text

UNIQUE 

Distinct11507
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size90.0 KiB
2024-04-03T14:35:01.430779image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length47
Median length47
Mean length47
Min length47

Characters and Unicode

Total characters540829
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11507 ?
Unique (%)100.0%

Sample

1st row1663969629-c5cb4fec-4e5a-4e34-8fd3-4b86d729dd80
2nd row1667093704-73130db4-c26b-4778-a242-0b98c46adabc
3rd row1662316101-ff0b283d-fbfe-497c-b139-2f0c0a48f0bd
4th row1665031867-37c024ab-8ea8-4a95-8f27-7bed6426c383
5th row1661703086-0acbaf03-f0dc-4884-bcfd-08bc33f68191
ValueCountFrequency (%)
1663969629-c5cb4fec-4e5a-4e34-8fd3-4b86d729dd80 1
 
< 0.1%
1663466027-3113b6c6-55b4-4958-809e-6855c793e511 1
 
< 0.1%
1662685945-93cf5579-b8ae-497b-a7dc-48934ed677ee 1
 
< 0.1%
1663000378-e79ed9aa-f382-405f-9e8d-7284e65a1046 1
 
< 0.1%
1662316101-ff0b283d-fbfe-497c-b139-2f0c0a48f0bd 1
 
< 0.1%
1665031867-37c024ab-8ea8-4a95-8f27-7bed6426c383 1
 
< 0.1%
1661703086-0acbaf03-f0dc-4884-bcfd-08bc33f68191 1
 
< 0.1%
1668745287-8002d22f-a396-4bf1-9a64-e3e8e3688fe9 1
 
< 0.1%
1669430529-d8660dd7-c937-4943-b368-a7038bc84b4e 1
 
< 0.1%
1667705666-7304b590-ace6-45e1-bb6c-ff784927d515 1
 
< 0.1%
Other values (11497) 11497
99.9%
2024-04-03T14:35:01.667324image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 57535
 
10.6%
6 48753
 
9.0%
4 41118
 
7.6%
1 40883
 
7.6%
5 33944
 
6.3%
8 32831
 
6.1%
9 32390
 
6.0%
3 29887
 
5.5%
7 29862
 
5.5%
0 29241
 
5.4%
Other values (7) 164385
30.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 540829
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 57535
 
10.6%
6 48753
 
9.0%
4 41118
 
7.6%
1 40883
 
7.6%
5 33944
 
6.3%
8 32831
 
6.1%
9 32390
 
6.0%
3 29887
 
5.5%
7 29862
 
5.5%
0 29241
 
5.4%
Other values (7) 164385
30.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 540829
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 57535
 
10.6%
6 48753
 
9.0%
4 41118
 
7.6%
1 40883
 
7.6%
5 33944
 
6.3%
8 32831
 
6.1%
9 32390
 
6.0%
3 29887
 
5.5%
7 29862
 
5.5%
0 29241
 
5.4%
Other values (7) 164385
30.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 540829
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 57535
 
10.6%
6 48753
 
9.0%
4 41118
 
7.6%
1 40883
 
7.6%
5 33944
 
6.3%
8 32831
 
6.1%
9 32390
 
6.0%
3 29887
 
5.5%
7 29862
 
5.5%
0 29241
 
5.4%
Other values (7) 164385
30.4%

start_time
Real number (ℝ)

UNIQUE 

Distinct11507
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6620282 × 109
Minimum1.6538179 × 109
Maximum1.6696757 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size90.0 KiB
2024-04-03T14:35:01.794628image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1.6538179 × 109
5-th percentile1.6554411 × 109
Q11.6585336 × 109
median1.662054 × 109
Q31.6654145 × 109
95-th percentile1.6687429 × 109
Maximum1.6696757 × 109
Range15857838
Interquartile range (IQR)6880890.1

Descriptive statistics

Standard deviation4185413.9
Coefficient of variation (CV)0.0025182569
Kurtosis-1.0798586
Mean1.6620282 × 109
Median Absolute Deviation (MAD)3451166.9
Skewness0.01663754
Sum1.9124958 × 1013
Variance1.7517689 × 1013
MonotonicityNot monotonic
2024-04-03T14:35:01.920407image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1663969630 1
 
< 0.1%
1657158975 1
 
< 0.1%
1668702014 1
 
< 0.1%
1662773034 1
 
< 0.1%
1664936635 1
 
< 0.1%
1669352443 1
 
< 0.1%
1662344125 1
 
< 0.1%
1666064426 1
 
< 0.1%
1657459849 1
 
< 0.1%
1655309692 1
 
< 0.1%
Other values (11497) 11497
99.9%
ValueCountFrequency (%)
1653817903 1
< 0.1%
1653869872 1
< 0.1%
1653870266 1
< 0.1%
1653872787 1
< 0.1%
1653896334 1
< 0.1%
1653898295 1
< 0.1%
1653898977 1
< 0.1%
1653924516 1
< 0.1%
1653925023 1
< 0.1%
1653931305 1
< 0.1%
ValueCountFrequency (%)
1669675741 1
< 0.1%
1669675208 1
< 0.1%
1669674609 1
< 0.1%
1669672729 1
< 0.1%
1669670779 1
< 0.1%
1669670000 1
< 0.1%
1669669753 1
< 0.1%
1669669707 1
< 0.1%
1669669617 1
< 0.1%
1669668659 1
< 0.1%
Distinct4099
Distinct (%)35.6%
Missing0
Missing (%)0.0%
Memory size90.0 KiB
2024-04-03T14:35:02.038589image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length198
Median length22
Mean length45.891023
Min length22

Characters and Unicode

Total characters528068
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3035 ?
Unique (%)26.4%

Sample

1st row['198813640578542210', '276755891880615063', '753313111965843135']
2nd row['177558997848408361']
3rd row['196558367709637569', '263880573407494191', '278328381340542754', '583320224080630228']
4th row['645474316434980766']
5th row['423192139884408071']
ValueCountFrequency (%)
156982178525322073 382
 
1.6%
483720663430960073 372
 
1.5%
956450128256143536 371
 
1.5%
252849683418778978 366
 
1.5%
321444462285149813 357
 
1.5%
192437459265741711 333
 
1.4%
317968008767343912 326
 
1.4%
264055759207672591 303
 
1.3%
257310588978331107 295
 
1.2%
287147463172552760 212
 
0.9%
Other values (1845) 20686
86.2%
2024-04-03T14:35:02.271824image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 49659
9.4%
1 49063
9.3%
' 48006
9.1%
7 46021
8.7%
3 45373
8.6%
6 42171
8.0%
4 40907
7.7%
8 40843
7.7%
5 40497
7.7%
0 39430
7.5%
Other values (5) 86098
16.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 528068
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 49659
9.4%
1 49063
9.3%
' 48006
9.1%
7 46021
8.7%
3 45373
8.6%
6 42171
8.0%
4 40907
7.7%
8 40843
7.7%
5 40497
7.7%
0 39430
7.5%
Other values (5) 86098
16.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 528068
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 49659
9.4%
1 49063
9.3%
' 48006
9.1%
7 46021
8.7%
3 45373
8.6%
6 42171
8.0%
4 40907
7.7%
8 40843
7.7%
5 40497
7.7%
0 39430
7.5%
Other values (5) 86098
16.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 528068
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 49659
9.4%
1 49063
9.3%
' 48006
9.1%
7 46021
8.7%
3 45373
8.6%
6 42171
8.0%
4 40907
7.7%
8 40843
7.7%
5 40497
7.7%
0 39430
7.5%
Other values (5) 86098
16.3%
Distinct10015
Distinct (%)87.0%
Missing0
Missing (%)0.0%
Memory size90.0 KiB
2024-04-03T14:35:02.471965image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length5004
Median length3208
Mean length786.39784
Min length346

Characters and Unicode

Total characters9049080
Distinct characters56
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9249 ?
Unique (%)80.4%

Sample

1st row[{'hp_ratio': (147, 147), 'class': [('Fighter', 13)], 'slots': {'1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'max_slots': {'1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'ac': 19, 'stats': {'prof_bonus': 5, 'strength': 20, 'dexterity': 12, 'constitution': 20, 'intelligence': 10, 'wisdom': 17, 'charisma': 10}}, {'hp_ratio': None, 'class': [('Paladin', 7), ('Fighter', 1)], 'slots': {'1': 4, '2': 3, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'max_slots': {'1': 4, '2': 3, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'ac': 19, 'stats': {'prof_bonus': 3, 'strength': 19, 'dexterity': 10, 'constitution': 16, 'intelligence': 9, 'wisdom': 12, 'charisma': 20}}, {'hp_ratio': (20, 58), 'class': [('Paladin', 6)], 'slots': {'1': 4, '2': 2, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'max_slots': {'1': 4, '2': 2, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'ac': 11, 'stats': {'prof_bonus': 3, 'strength': 16, 'dexterity': 12, 'constitution': 16, 'intelligence': 10, 'wisdom': 10, 'charisma': 20}}]
2nd row[{'hp_ratio': (0, 23), 'class': [('Warlock', 4)], 'slots': {'1': 0, '2': 2, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'max_slots': {'1': 0, '2': 2, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'ac': 19, 'stats': {'prof_bonus': 2, 'strength': 20, 'dexterity': 9, 'constitution': 10, 'intelligence': 12, 'wisdom': 14, 'charisma': 19}}]
3rd row[{'hp_ratio': (133, 238), 'class': [('Fighter', 18)], 'slots': {'1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'max_slots': {'1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'ac': 19, 'stats': {'prof_bonus': 6, 'strength': 20, 'dexterity': 8, 'constitution': 20, 'intelligence': 9, 'wisdom': 13, 'charisma': 14}}, {'hp_ratio': (116, 116), 'class': [('Blood Hunter', 16)], 'slots': {'1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'max_slots': {'1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'ac': 17, 'stats': {'prof_bonus': 5, 'strength': 11, 'dexterity': 20, 'constitution': 12, 'intelligence': 11, 'wisdom': 18, 'charisma': 9}}, {'hp_ratio': (117, 155), 'class': [('Monk', 13), ('Cleric', 6)], 'slots': {'1': 4, '2': 3, '3': 3, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'max_slots': {'1': 4, '2': 3, '3': 3, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'ac': 21, 'stats': {'prof_bonus': 6, 'strength': 10, 'dexterity': 20, 'constitution': 16, 'intelligence': 9, 'wisdom': 20, 'charisma': 13}}, {'hp_ratio': None, 'class': [('Sorcerer', 16), ('Bard', 3)], 'slots': {'1': 4, '2': 3, '3': 3, '4': 3, '5': 3, '6': 2, '7': 1, '8': 1, '9': 1}, 'max_slots': {'1': 4, '2': 3, '3': 3, '4': 3, '5': 3, '6': 2, '7': 1, '8': 1, '9': 1}, 'ac': 19, 'stats': {'prof_bonus': 6, 'strength': 14, 'dexterity': 10, 'constitution': 20, 'intelligence': 8, 'wisdom': 13, 'charisma': 20}}]
4th row[{'hp_ratio': (107, 107), 'class': [('Druid', 13)], 'slots': {'1': 4, '2': 3, '3': 3, '4': 3, '5': 2, '6': 1, '7': 1, '8': 0, '9': 0}, 'max_slots': {'1': 4, '2': 3, '3': 3, '4': 3, '5': 2, '6': 1, '7': 1, '8': 0, '9': 0}, 'ac': 19, 'stats': {'prof_bonus': 5, 'strength': 10, 'dexterity': 16, 'constitution': 16, 'intelligence': 10, 'wisdom': 20, 'charisma': 12}}]
5th row[{'hp_ratio': (147, 147), 'class': [('Fighter', 13)], 'slots': {'1': 0, '2': 1, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'max_slots': {'1': 0, '2': 1, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'ac': 22, 'stats': {'prof_bonus': 5, 'strength': 20, 'dexterity': 8, 'constitution': 20, 'intelligence': 12, 'wisdom': 13, 'charisma': 10}}]
ValueCountFrequency (%)
0 346484
22.8%
3 103545
 
6.8%
2 81161
 
5.3%
4 78033
 
5.1%
1 68187
 
4.5%
8 61812
 
4.1%
5 57464
 
3.8%
6 56050
 
3.7%
9 55932
 
3.7%
7 52429
 
3.4%
Other values (329) 561610
36.9%
2024-04-03T14:35:02.872088image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
' 1596728
17.6%
1511200
16.7%
: 763871
 
8.4%
, 746699
 
8.3%
0 383647
 
4.2%
t 325991
 
3.6%
s 320333
 
3.5%
1 238723
 
2.6%
i 215660
 
2.4%
o 210917
 
2.3%
Other values (46) 2735311
30.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9049080
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
' 1596728
17.6%
1511200
16.7%
: 763871
 
8.4%
, 746699
 
8.3%
0 383647
 
4.2%
t 325991
 
3.6%
s 320333
 
3.5%
1 238723
 
2.6%
i 215660
 
2.4%
o 210917
 
2.3%
Other values (46) 2735311
30.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9049080
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
' 1596728
17.6%
1511200
16.7%
: 763871
 
8.4%
, 746699
 
8.3%
0 383647
 
4.2%
t 325991
 
3.6%
s 320333
 
3.5%
1 238723
 
2.6%
i 215660
 
2.4%
o 210917
 
2.3%
Other values (46) 2735311
30.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9049080
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
' 1596728
17.6%
1511200
16.7%
: 763871
 
8.4%
, 746699
 
8.3%
0 383647
 
4.2%
t 325991
 
3.6%
s 320333
 
3.5%
1 238723
 
2.6%
i 215660
 
2.4%
o 210917
 
2.3%
Other values (46) 2735311
30.2%

monsters_info
Text

UNIQUE 

Distinct11507
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size90.0 KiB
2024-04-03T14:35:03.062109image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length23938
Median length5835
Mean length421.47223
Min length115

Characters and Unicode

Total characters4849881
Distinct characters160
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11507 ?
Unique (%)100.0%

Sample

1st row[{'monster_id': 'af27cde4-5fca-42ed-a676-3ffec286601c', 'monster_code': 'GH1', 'monster_name': 'Ghoul', 'level': 1.0}, {'monster_id': '5f73b515-afca-48fa-9b87-3f53d718cc57', 'monster_code': 'NS1', 'monster_name': 'Nightveil Specter', 'level': 10.0}, {'monster_id': 'b9b9e516-a047-4db0-922c-080060c76383', 'monster_code': 'GH1', 'monster_name': 'Ghoul', 'level': 1.0}, {'monster_id': '64f8d6c2-8c1a-4896-a0e4-7fb2f4f1f526', 'monster_code': 'WR1', 'monster_name': 'Wraith', 'level': 5.0}, {'monster_id': 'abfed520-dd1e-4d1c-af63-d0818be5d09f', 'monster_code': 'MU1', 'monster_name': 'Mummy', 'level': 3.0}, {'monster_id': 'b62e450b-07dc-4e02-a5b0-6543b4ec02a9', 'monster_code': 'GH1', 'monster_name': 'Ghoul', 'level': 1.0}, {'monster_id': '76092654-606a-4cff-9160-4944ae1c1780', 'monster_code': 'BO1', 'monster_name': 'Boneless', 'level': 1.0}, {'monster_id': '2ef9d2a7-3751-48c5-b4e5-8b2a36e9c55a', 'monster_code': 'MA1', 'monster_name': 'Manes', 'level': 0.125}, {'monster_id': '6d1d38e8-b118-4bb7-8cfe-7c356d55f519', 'monster_code': 'VR1', 'monster_name': 'Vrock', 'level': 6.0}, {'monster_id': '70f6ee1e-ab05-4879-af89-3df127b696d3', 'monster_code': 'BD1', 'monster_name': 'Bearded Devil', 'level': 3.0}, {'monster_id': '4cfa2697-e6cf-4b89-b4aa-98dbc66f93da', 'monster_code': 'YMZ1', 'monster_name': 'Yellow Musk Zombie', 'level': 0.25}]
2nd row[{'monster_id': '4c7d90bb-9255-44a9-aa4e-f6ffa1fa9000', 'monster_code': 'GR1', 'monster_name': 'Giant Rat', 'level': 0.125}, {'monster_id': 'b5491d16-ca83-4996-82d1-41d1ec1b7db6', 'monster_code': 'GR2', 'monster_name': 'Giant Rat', 'level': 0.125}, {'monster_id': '6ede1067-ed54-42bb-9ce2-14068477164c', 'monster_code': 'GR3', 'monster_name': 'Giant Rat', 'level': 0.125}, {'monster_id': '15406585-e135-4bde-b13a-95e9039052c4', 'monster_code': 'GR1', 'monster_name': 'Giant Rat', 'level': 0.125}, {'monster_id': 'cba094e4-cbda-4cb3-b96d-6783a378cba9', 'monster_code': 'GR3', 'monster_name': 'Giant Rat', 'level': 0.125}, {'monster_id': '535a9ef0-3c3c-4719-824c-761ee6ba0675', 'monster_code': 'SoR1', 'monster_name': 'Swarm of Rats', 'level': 0.25}, {'monster_id': '18ae3599-3f5f-434c-abf4-08985400956c', 'monster_code': 'GR1', 'monster_name': 'Giant Rat', 'level': 0.125}, {'monster_id': '5b665adc-16fb-4a81-93da-3476c692d470', 'monster_code': 'GR2', 'monster_name': 'Giant Rat', 'level': 0.125}, {'monster_id': '5369b5d1-45e9-4242-8a9b-1d4280822a1e', 'monster_code': 'GR3', 'monster_name': 'Giant Rat', 'level': 0.125}, {'monster_id': '5a49cbdf-9890-4826-bb02-98a9f60cf367', 'monster_code': 'SoR1', 'monster_name': 'Swarm of Rats', 'level': 0.25}, {'monster_id': 'f752f71e-0840-4f4c-af8e-73cd169acf8a', 'monster_code': 'GR1', 'monster_name': 'Giant Rat', 'level': 0.125}, {'monster_id': 'ff186863-f2ab-40f0-b194-8e2f4965b444', 'monster_code': 'GR2', 'monster_name': 'Giant Rat', 'level': 0.125}, {'monster_id': '62baee43-b320-414a-b2fb-1010a1635bf0', 'monster_code': 'GR3', 'monster_name': 'Giant Rat', 'level': 0.125}, {'monster_id': '0162fa37-9f5a-4be5-b820-437ef078ae4e', 'monster_code': 'SoR1', 'monster_name': 'Swarm of Rats', 'level': 0.25}, {'monster_id': 'd1084b11-b70b-4b0b-826d-ef22e6237863', 'monster_code': 'SoR2', 'monster_name': 'Swarm of Rats', 'level': 0.25}]
3rd row[{'monster_id': '5b6213ac-cff6-416c-a8f0-784730069478', 'monster_code': 'ARD1', 'monster_name': 'Adult Red Dragon', 'level': 17.0}, {'monster_id': '27bec593-43b4-45b5-894c-59e88426f0e6', 'monster_code': 'Galileo', 'monster_name': 'Aberrant Spirit', 'level': 0.125}, {'monster_id': '9ab5e823-ad13-410d-900d-be441f25fdf4', 'monster_code': 'FG2', 'monster_name': 'Frost Giant', 'level': 8.0}, {'monster_id': '41241090-6ed8-4b07-a8a3-3b9acde4d593', 'monster_code': 'FG1', 'monster_name': 'Frost Giant', 'level': 8.0}, {'monster_id': '9d8e5263-4270-449b-8286-72980342d5ee', 'monster_code': 'BU1', 'monster_name': 'Bulette', 'level': 5.0}, {'monster_id': 'a2f438da-b6c0-4ed0-b1f3-73954c416080', 'monster_code': 'BU2', 'monster_name': 'Bulette', 'level': 5.0}, {'monster_id': 'b6c2dfcf-f5d4-47f4-a9fe-5063a5609d97', 'monster_code': 'BU3', 'monster_name': 'Bulette', 'level': 5.0}, {'monster_id': '467c09bd-9a77-472a-a881-4694e963f20a', 'monster_code': 'YRD1', 'monster_name': 'Young Red Dragon', 'level': 10.0}]
4th row[{'monster_id': 'b2cd27b1-6e9e-42cb-bd32-9babc3c733eb', 'monster_code': 'TNC1', 'monster_name': 'Tooth-N-Claw', 'level': 3.0}, {'monster_id': '82c5d818-6226-43fd-967f-caa4cce78f2b', 'monster_code': 'SPB1', 'monster_name': 'Skeletal Polar Bear', 'level': 2.0}, {'monster_id': '4c203c45-e419-4e01-bfd8-d7dbe6b220cb', 'monster_code': 'MI1', 'monster_name': 'Mimic', 'level': 2.0}, {'monster_id': '7b68f331-3930-40f6-9ea2-199df1b2ce4b', 'monster_code': 'TA1', 'monster_name': 'Tanarukk', 'level': 5.0}]
5th row[{'monster_id': '7c3aef0b-1606-46a7-92c9-5a43e46ab5ba', 'monster_code': 'ML1', 'monster_name': 'Mummy Lord', 'level': 15.0}]
ValueCountFrequency (%)
level 39223
 
11.6%
monster_id 39171
 
11.6%
monster_code 39171
 
11.6%
monster_name 39171
 
11.6%
0.25 5332
 
1.6%
1.0 3849
 
1.1%
2.0 3787
 
1.1%
0.125 3722
 
1.1%
0.5 3262
 
1.0%
6.0 2852
 
0.8%
Other values (47258) 157305
46.7%
2024-04-03T14:35:03.357144image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
' 548143
 
11.3%
e 382842
 
7.9%
325340
 
6.7%
o 185801
 
3.8%
n 180629
 
3.7%
m 165643
 
3.4%
d 163276
 
3.4%
- 157543
 
3.2%
a 157155
 
3.2%
: 156693
 
3.2%
Other values (150) 2426816
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4849881
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
' 548143
 
11.3%
e 382842
 
7.9%
325340
 
6.7%
o 185801
 
3.8%
n 180629
 
3.7%
m 165643
 
3.4%
d 163276
 
3.4%
- 157543
 
3.2%
a 157155
 
3.2%
: 156693
 
3.2%
Other values (150) 2426816
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4849881
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
' 548143
 
11.3%
e 382842
 
7.9%
325340
 
6.7%
o 185801
 
3.8%
n 180629
 
3.7%
m 165643
 
3.4%
d 163276
 
3.4%
- 157543
 
3.2%
a 157155
 
3.2%
: 156693
 
3.2%
Other values (150) 2426816
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4849881
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
' 548143
 
11.3%
e 382842
 
7.9%
325340
 
6.7%
o 185801
 
3.8%
n 180629
 
3.7%
m 165643
 
3.4%
d 163276
 
3.4%
- 157543
 
3.2%
a 157155
 
3.2%
: 156693
 
3.2%
Other values (150) 2426816
50.0%

party_size
Real number (ℝ)

HIGH CORRELATION 

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0859477
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size90.0 KiB
2024-04-03T14:35:03.455396image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q33
95-th percentile5
Maximum9
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.5433928
Coefficient of variation (CV)0.73990005
Kurtosis0.93529415
Mean2.0859477
Median Absolute Deviation (MAD)0
Skewness1.3594551
Sum24003
Variance2.3820613
MonotonicityNot monotonic
2024-04-03T14:35:03.530378image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 6391
55.5%
2 1913
 
16.6%
4 949
 
8.2%
3 928
 
8.1%
5 902
 
7.8%
6 316
 
2.7%
7 74
 
0.6%
8 24
 
0.2%
9 10
 
0.1%
ValueCountFrequency (%)
1 6391
55.5%
2 1913
 
16.6%
3 928
 
8.1%
4 949
 
8.2%
5 902
 
7.8%
6 316
 
2.7%
7 74
 
0.6%
8 24
 
0.2%
9 10
 
0.1%
ValueCountFrequency (%)
9 10
 
0.1%
8 24
 
0.2%
7 74
 
0.6%
6 316
 
2.7%
5 902
 
7.8%
4 949
 
8.2%
3 928
 
8.1%
2 1913
 
16.6%
1 6391
55.5%
Distinct2252
Distinct (%)19.6%
Missing0
Missing (%)0.0%
Memory size90.0 KiB
2024-04-03T14:35:03.596829image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length77
Median length72
Mean length72.208395
Min length72

Characters and Unicode

Total characters830902
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1618 ?
Unique (%)14.1%

Sample

1st row{'1': 8, '2': 5, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}
2nd row{'1': 0, '2': 2, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}
3rd row{'1': 8, '2': 6, '3': 6, '4': 3, '5': 3, '6': 2, '7': 1, '8': 1, '9': 1}
4th row{'1': 4, '2': 3, '3': 3, '4': 3, '5': 2, '6': 1, '7': 1, '8': 0, '9': 0}
5th row{'1': 0, '2': 1, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}
ValueCountFrequency (%)
0 71689
34.6%
3 20049
 
9.7%
2 16927
 
8.2%
1 16403
 
7.9%
4 15510
 
7.5%
6 13802
 
6.7%
5 13055
 
6.3%
8 12855
 
6.2%
7 12240
 
5.9%
9 12198
 
5.9%
Other values (24) 2398
 
1.2%
2024-04-03T14:35:03.758504image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
' 207126
24.9%
195619
23.5%
: 103563
12.5%
, 92056
11.1%
0 72138
 
8.7%
3 20206
 
2.4%
1 19102
 
2.3%
2 17639
 
2.1%
4 15718
 
1.9%
6 13991
 
1.7%
Other values (6) 73744
 
8.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 830902
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
' 207126
24.9%
195619
23.5%
: 103563
12.5%
, 92056
11.1%
0 72138
 
8.7%
3 20206
 
2.4%
1 19102
 
2.3%
2 17639
 
2.1%
4 15718
 
1.9%
6 13991
 
1.7%
Other values (6) 73744
 
8.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 830902
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
' 207126
24.9%
195619
23.5%
: 103563
12.5%
, 92056
11.1%
0 72138
 
8.7%
3 20206
 
2.4%
1 19102
 
2.3%
2 17639
 
2.1%
4 15718
 
1.9%
6 13991
 
1.7%
Other values (6) 73744
 
8.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 830902
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
' 207126
24.9%
195619
23.5%
: 103563
12.5%
, 92056
11.1%
0 72138
 
8.7%
3 20206
 
2.4%
1 19102
 
2.3%
2 17639
 
2.1%
4 15718
 
1.9%
6 13991
 
1.7%
Other values (6) 73744
 
8.9%
Distinct1525
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Memory size90.0 KiB
2024-04-03T14:35:03.839582image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length77
Median length72
Mean length72.240289
Min length72

Characters and Unicode

Total characters831269
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique950 ?
Unique (%)8.3%

Sample

1st row{'1': 8, '2': 5, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}
2nd row{'1': 0, '2': 2, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}
3rd row{'1': 8, '2': 6, '3': 6, '4': 3, '5': 3, '6': 2, '7': 1, '8': 1, '9': 1}
4th row{'1': 4, '2': 3, '3': 3, '4': 3, '5': 2, '6': 1, '7': 1, '8': 0, '9': 0}
5th row{'1': 0, '2': 1, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}
ValueCountFrequency (%)
0 70694
34.1%
3 20745
 
10.0%
2 16589
 
8.0%
1 15981
 
7.7%
4 15711
 
7.6%
6 14113
 
6.8%
8 13166
 
6.4%
5 12963
 
6.3%
9 12336
 
6.0%
7 12063
 
5.8%
Other values (26) 2765
 
1.3%
2024-04-03T14:35:04.000594image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
' 207126
24.9%
195619
23.5%
: 103563
12.5%
, 92056
11.1%
0 71126
 
8.6%
3 20925
 
2.5%
1 19002
 
2.3%
2 17544
 
2.1%
4 15977
 
1.9%
6 14356
 
1.7%
Other values (6) 73975
 
8.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 831269
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
' 207126
24.9%
195619
23.5%
: 103563
12.5%
, 92056
11.1%
0 71126
 
8.6%
3 20925
 
2.5%
1 19002
 
2.3%
2 17544
 
2.1%
4 15977
 
1.9%
6 14356
 
1.7%
Other values (6) 73975
 
8.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 831269
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
' 207126
24.9%
195619
23.5%
: 103563
12.5%
, 92056
11.1%
0 71126
 
8.6%
3 20925
 
2.5%
1 19002
 
2.3%
2 17544
 
2.1%
4 15977
 
1.9%
6 14356
 
1.7%
Other values (6) 73975
 
8.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 831269
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
' 207126
24.9%
195619
23.5%
: 103563
12.5%
, 92056
11.1%
0 71126
 
8.6%
3 20925
 
2.5%
1 19002
 
2.3%
2 17544
 
2.1%
4 15977
 
1.9%
6 14356
 
1.7%
Other values (6) 73975
 
8.9%
Distinct5867
Distinct (%)51.0%
Missing0
Missing (%)0.0%
Memory size90.0 KiB
2024-04-03T14:35:04.098269image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length288
Median length201
Mean length46.75667
Min length2

Characters and Unicode

Total characters538029
Distinct characters43
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4716 ?
Unique (%)41.0%

Sample

1st row[('Fighter', 13), ('Paladin', 7), ('Fighter', 1), ('Paladin', 6)]
2nd row[('Warlock', 4)]
3rd row[('Fighter', 18), ('Blood Hunter', 16), ('Monk', 13), ('Cleric', 6), ('Sorcerer', 16), ('Bard', 3)]
4th row[('Druid', 13)]
5th row[('Fighter', 13)]
ValueCountFrequency (%)
fighter 5037
 
7.2%
1 4493
 
6.5%
3 4483
 
6.4%
2 4224
 
6.1%
5 4049
 
5.8%
4 3897
 
5.6%
warlock 3635
 
5.2%
rogue 3450
 
5.0%
cleric 3397
 
4.9%
6 3151
 
4.5%
Other values (25) 29808
42.8%
2024-04-03T14:35:04.310869image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
' 68986
 
12.8%
58117
 
10.8%
, 57487
 
10.7%
( 34493
 
6.4%
) 34493
 
6.4%
r 34017
 
6.3%
a 23462
 
4.4%
e 19687
 
3.7%
i 18532
 
3.4%
o 12990
 
2.4%
Other values (33) 175765
32.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 538029
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
' 68986
 
12.8%
58117
 
10.8%
, 57487
 
10.7%
( 34493
 
6.4%
) 34493
 
6.4%
r 34017
 
6.3%
a 23462
 
4.4%
e 19687
 
3.7%
i 18532
 
3.4%
o 12990
 
2.4%
Other values (33) 175765
32.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 538029
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
' 68986
 
12.8%
58117
 
10.8%
, 57487
 
10.7%
( 34493
 
6.4%
) 34493
 
6.4%
r 34017
 
6.3%
a 23462
 
4.4%
e 19687
 
3.7%
i 18532
 
3.4%
o 12990
 
2.4%
Other values (33) 175765
32.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 538029
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
' 68986
 
12.8%
58117
 
10.8%
, 57487
 
10.7%
( 34493
 
6.4%
) 34493
 
6.4%
r 34017
 
6.3%
a 23462
 
4.4%
e 19687
 
3.7%
i 18532
 
3.4%
o 12990
 
2.4%
Other values (33) 175765
32.7%
Distinct3903
Distinct (%)33.9%
Missing0
Missing (%)0.0%
Memory size90.0 KiB
2024-04-03T14:35:04.399093image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length198
Median length137
Mean length31.313983
Min length2

Characters and Unicode

Total characters360330
Distinct characters31
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3331 ?
Unique (%)28.9%

Sample

1st row['Fighter', 'Paladin', 'Fighter', 'Paladin']
2nd row['Warlock']
3rd row['Fighter', 'Blood Hunter', 'Monk', 'Cleric', 'Sorcerer', 'Bard']
4th row['Druid']
5th row['Fighter']
ValueCountFrequency (%)
fighter 5037
14.3%
warlock 3635
10.3%
rogue 3450
9.8%
cleric 3397
9.7%
wizard 3129
8.9%
paladin 2899
8.3%
sorcerer 2700
7.7%
barbarian 2436
6.9%
monk 1963
 
5.6%
bard 1810
 
5.2%
Other values (5) 4675
13.3%
2024-04-03T14:35:04.609633image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
' 68986
19.1%
r 34017
 
9.4%
23624
 
6.6%
a 23462
 
6.5%
, 22994
 
6.4%
e 19687
 
5.5%
i 18532
 
5.1%
o 12990
 
3.6%
] 11507
 
3.2%
[ 11507
 
3.2%
Other values (21) 113024
31.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 360330
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
' 68986
19.1%
r 34017
 
9.4%
23624
 
6.6%
a 23462
 
6.5%
, 22994
 
6.4%
e 19687
 
5.5%
i 18532
 
5.1%
o 12990
 
3.6%
] 11507
 
3.2%
[ 11507
 
3.2%
Other values (21) 113024
31.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 360330
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
' 68986
19.1%
r 34017
 
9.4%
23624
 
6.6%
a 23462
 
6.5%
, 22994
 
6.4%
e 19687
 
5.5%
i 18532
 
5.1%
o 12990
 
3.6%
] 11507
 
3.2%
[ 11507
 
3.2%
Other values (21) 113024
31.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 360330
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
' 68986
19.1%
r 34017
 
9.4%
23624
 
6.6%
a 23462
 
6.5%
, 22994
 
6.4%
e 19687
 
5.5%
i 18532
 
5.1%
o 12990
 
3.6%
] 11507
 
3.2%
[ 11507
 
3.2%
Other values (21) 113024
31.4%
Distinct7460
Distinct (%)64.8%
Missing0
Missing (%)0.0%
Memory size90.0 KiB
2024-04-03T14:35:04.793370image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length103
Median length93
Mean length21.241853
Min length8

Characters and Unicode

Total characters244430
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6478 ?
Unique (%)56.3%

Sample

1st row[(147, 147), (20, 58)]
2nd row[(0, 23)]
3rd row[(133, 238), (116, 116), (117, 155)]
4th row[(107, 107)]
5th row[(147, 147)]
ValueCountFrequency (%)
31 1109
 
2.3%
38 1024
 
2.1%
0 1008
 
2.1%
44 930
 
2.0%
24 917
 
1.9%
27 907
 
1.9%
45 876
 
1.8%
52 806
 
1.7%
43 805
 
1.7%
32 761
 
1.6%
Other values (300) 38541
80.8%
2024-04-03T14:35:05.088718image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 36177
14.8%
36177
14.8%
( 23842
9.8%
) 23842
9.8%
1 16974
 
6.9%
3 13689
 
5.6%
2 12684
 
5.2%
4 12157
 
5.0%
[ 11507
 
4.7%
] 11507
 
4.7%
Other values (6) 45874
18.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 244430
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
, 36177
14.8%
36177
14.8%
( 23842
9.8%
) 23842
9.8%
1 16974
 
6.9%
3 13689
 
5.6%
2 12684
 
5.2%
4 12157
 
5.0%
[ 11507
 
4.7%
] 11507
 
4.7%
Other values (6) 45874
18.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 244430
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
, 36177
14.8%
36177
14.8%
( 23842
9.8%
) 23842
9.8%
1 16974
 
6.9%
3 13689
 
5.6%
2 12684
 
5.2%
4 12157
 
5.0%
[ 11507
 
4.7%
] 11507
 
4.7%
Other values (6) 45874
18.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 244430
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
, 36177
14.8%
36177
14.8%
( 23842
9.8%
) 23842
9.8%
1 16974
 
6.9%
3 13689
 
5.6%
2 12684
 
5.2%
4 12157
 
5.0%
[ 11507
 
4.7%
] 11507
 
4.7%
Other values (6) 45874
18.8%
Distinct3134
Distinct (%)27.2%
Missing0
Missing (%)0.0%
Memory size90.0 KiB
2024-04-03T14:35:05.185425image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length56
Median length4
Mean length8.5636569
Min length3

Characters and Unicode

Total characters98542
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2658 ?
Unique (%)23.1%

Sample

1st row[19, 19, 11]
2nd row[19]
3rd row[19, 17, 21, 19]
4th row[19]
5th row[22]
ValueCountFrequency (%)
18 4131
16.8%
16 3332
13.5%
17 3114
12.6%
19 2540
10.3%
15 2358
9.6%
14 1895
7.7%
20 1729
7.0%
13 1202
 
4.9%
12 932
 
3.8%
21 847
 
3.4%
Other values (18) 2561
10.4%
2024-04-03T14:35:05.359355image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 21458
21.8%
, 13134
13.3%
13134
13.3%
[ 11507
11.7%
] 11507
11.7%
2 6142
 
6.2%
8 4152
 
4.2%
6 3448
 
3.5%
7 3139
 
3.2%
9 2562
 
2.6%
Other values (4) 8359
 
8.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 98542
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 21458
21.8%
, 13134
13.3%
13134
13.3%
[ 11507
11.7%
] 11507
11.7%
2 6142
 
6.2%
8 4152
 
4.2%
6 3448
 
3.5%
7 3139
 
3.2%
9 2562
 
2.6%
Other values (4) 8359
 
8.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 98542
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 21458
21.8%
, 13134
13.3%
13134
13.3%
[ 11507
11.7%
] 11507
11.7%
2 6142
 
6.2%
8 4152
 
4.2%
6 3448
 
3.5%
7 3139
 
3.2%
9 2562
 
2.6%
Other values (4) 8359
 
8.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 98542
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 21458
21.8%
, 13134
13.3%
13134
13.3%
[ 11507
11.7%
] 11507
11.7%
2 6142
 
6.2%
8 4152
 
4.2%
6 3448
 
3.5%
7 3139
 
3.2%
9 2562
 
2.6%
Other values (4) 8359
 
8.5%
Distinct686
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size90.0 KiB
2024-04-03T14:35:05.426410image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length42
Median length3
Mean length6.4241766
Min length3

Characters and Unicode

Total characters73923
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique419 ?
Unique (%)3.6%

Sample

1st row[5, 3, 3]
2nd row[2]
3rd row[6, 5, 6, 6]
4th row[5]
5th row[5]
ValueCountFrequency (%)
3 9725
39.5%
2 6578
26.7%
4 4246
17.2%
5 2306
 
9.4%
6 1755
 
7.1%
7 30
 
0.1%
1 1
 
< 0.1%
2024-04-03T14:35:05.587995image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 13134
17.8%
13134
17.8%
[ 11507
15.6%
] 11507
15.6%
3 9725
13.2%
2 6578
8.9%
4 4246
 
5.7%
5 2306
 
3.1%
6 1755
 
2.4%
7 30
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 73923
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
, 13134
17.8%
13134
17.8%
[ 11507
15.6%
] 11507
15.6%
3 9725
13.2%
2 6578
8.9%
4 4246
 
5.7%
5 2306
 
3.1%
6 1755
 
2.4%
7 30
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 73923
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
, 13134
17.8%
13134
17.8%
[ 11507
15.6%
] 11507
15.6%
3 9725
13.2%
2 6578
8.9%
4 4246
 
5.7%
5 2306
 
3.1%
6 1755
 
2.4%
7 30
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 73923
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
, 13134
17.8%
13134
17.8%
[ 11507
15.6%
] 11507
15.6%
3 9725
13.2%
2 6578
8.9%
4 4246
 
5.7%
5 2306
 
3.1%
6 1755
 
2.4%
7 30
 
< 0.1%
Distinct3208
Distinct (%)27.9%
Missing0
Missing (%)0.0%
Memory size90.0 KiB
2024-04-03T14:35:05.669141image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length53
Median length47
Mean length7.9876597
Min length3

Characters and Unicode

Total characters91914
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2772 ?
Unique (%)24.1%

Sample

1st row[20, 19, 16]
2nd row[20]
3rd row[20, 11, 10, 14]
4th row[10]
5th row[20]
ValueCountFrequency (%)
8 4065
16.5%
10 3251
13.2%
18 2159
8.8%
9 1752
7.1%
16 1745
7.1%
13 1659
6.7%
20 1600
 
6.5%
12 1538
 
6.2%
11 1497
 
6.1%
14 1363
 
5.5%
Other values (11) 4012
16.3%
2024-04-03T14:35:05.851938image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 17836
19.4%
, 13134
14.3%
13134
14.3%
[ 11507
12.5%
] 11507
12.5%
8 6224
 
6.8%
0 4851
 
5.3%
2 3489
 
3.8%
9 2932
 
3.2%
6 1922
 
2.1%
Other values (4) 5378
 
5.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 91914
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 17836
19.4%
, 13134
14.3%
13134
14.3%
[ 11507
12.5%
] 11507
12.5%
8 6224
 
6.8%
0 4851
 
5.3%
2 3489
 
3.8%
9 2932
 
3.2%
6 1922
 
2.1%
Other values (4) 5378
 
5.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 91914
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 17836
19.4%
, 13134
14.3%
13134
14.3%
[ 11507
12.5%
] 11507
12.5%
8 6224
 
6.8%
0 4851
 
5.3%
2 3489
 
3.8%
9 2932
 
3.2%
6 1922
 
2.1%
Other values (4) 5378
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 91914
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 17836
19.4%
, 13134
14.3%
13134
14.3%
[ 11507
12.5%
] 11507
12.5%
8 6224
 
6.8%
0 4851
 
5.3%
2 3489
 
3.8%
9 2932
 
3.2%
6 1922
 
2.1%
Other values (4) 5378
 
5.9%
Distinct3026
Distinct (%)26.3%
Missing0
Missing (%)0.0%
Memory size90.0 KiB
2024-04-03T14:35:05.935931image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length55
Median length4
Mean length8.4854436
Min length3

Characters and Unicode

Total characters97642
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2577 ?
Unique (%)22.4%

Sample

1st row[12, 10, 12]
2nd row[9]
3rd row[8, 20, 20, 10]
4th row[16]
5th row[8]
ValueCountFrequency (%)
14 5005
20.3%
16 3737
15.2%
18 3167
12.9%
20 3020
12.3%
12 1746
 
7.1%
13 1374
 
5.6%
10 1297
 
5.3%
17 1261
 
5.1%
15 1252
 
5.1%
11 901
 
3.7%
Other values (9) 1881
 
7.6%
2024-04-03T14:35:06.114399image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 21497
22.0%
, 13134
13.5%
13134
13.5%
[ 11507
11.8%
] 11507
11.8%
2 5019
 
5.1%
4 5005
 
5.1%
0 4317
 
4.4%
6 3898
 
4.0%
8 3561
 
3.6%
Other values (4) 5063
 
5.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 97642
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 21497
22.0%
, 13134
13.5%
13134
13.5%
[ 11507
11.8%
] 11507
11.8%
2 5019
 
5.1%
4 5005
 
5.1%
0 4317
 
4.4%
6 3898
 
4.0%
8 3561
 
3.6%
Other values (4) 5063
 
5.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 97642
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 21497
22.0%
, 13134
13.5%
13134
13.5%
[ 11507
11.8%
] 11507
11.8%
2 5019
 
5.1%
4 5005
 
5.1%
0 4317
 
4.4%
6 3898
 
4.0%
8 3561
 
3.6%
Other values (4) 5063
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 97642
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 21497
22.0%
, 13134
13.5%
13134
13.5%
[ 11507
11.8%
] 11507
11.8%
2 5019
 
5.1%
4 5005
 
5.1%
0 4317
 
4.4%
6 3898
 
4.0%
8 3561
 
3.6%
Other values (4) 5063
 
5.2%
Distinct2678
Distinct (%)23.3%
Missing0
Missing (%)0.0%
Memory size90.0 KiB
2024-04-03T14:35:06.195971image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length56
Median length4
Mean length8.555488
Min length3

Characters and Unicode

Total characters98448
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2153 ?
Unique (%)18.7%

Sample

1st row[20, 16, 16]
2nd row[10]
3rd row[20, 12, 16, 20]
4th row[16]
5th row[20]
ValueCountFrequency (%)
16 5912
24.0%
14 5718
23.2%
18 2873
11.7%
15 2441
9.9%
13 1688
 
6.9%
12 1539
 
6.2%
17 1449
 
5.9%
20 1376
 
5.6%
19 786
 
3.2%
10 411
 
1.7%
Other values (7) 448
 
1.8%
2024-04-03T14:35:06.366653image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 23381
23.7%
, 13134
13.3%
13134
13.3%
[ 11507
11.7%
] 11507
11.7%
6 5912
 
6.0%
4 5724
 
5.8%
2 3015
 
3.1%
8 2919
 
3.0%
5 2441
 
2.5%
Other values (4) 5774
 
5.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 98448
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 23381
23.7%
, 13134
13.3%
13134
13.3%
[ 11507
11.7%
] 11507
11.7%
6 5912
 
6.0%
4 5724
 
5.8%
2 3015
 
3.1%
8 2919
 
3.0%
5 2441
 
2.5%
Other values (4) 5774
 
5.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 98448
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 23381
23.7%
, 13134
13.3%
13134
13.3%
[ 11507
11.7%
] 11507
11.7%
6 5912
 
6.0%
4 5724
 
5.8%
2 3015
 
3.1%
8 2919
 
3.0%
5 2441
 
2.5%
Other values (4) 5774
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 98448
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 23381
23.7%
, 13134
13.3%
13134
13.3%
[ 11507
11.7%
] 11507
11.7%
6 5912
 
6.0%
4 5724
 
5.8%
2 3015
 
3.1%
8 2919
 
3.0%
5 2441
 
2.5%
Other values (4) 5774
 
5.9%
Distinct3033
Distinct (%)26.4%
Missing0
Missing (%)0.0%
Memory size90.0 KiB
2024-04-03T14:35:06.447138image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length53
Median length45
Mean length8.1317459
Min length3

Characters and Unicode

Total characters93572
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2578 ?
Unique (%)22.4%

Sample

1st row[10, 9, 10]
2nd row[12]
3rd row[9, 11, 9, 8]
4th row[10]
5th row[12]
ValueCountFrequency (%)
10 4830
19.6%
8 3048
12.4%
12 3035
12.3%
11 2575
10.5%
14 2173
8.8%
13 1638
 
6.6%
9 1637
 
6.6%
20 1347
 
5.5%
16 1168
 
4.7%
18 1042
 
4.2%
Other values (8) 2148
8.7%
2024-04-03T14:35:06.629235image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 20782
22.2%
, 13134
14.0%
13134
14.0%
[ 11507
12.3%
] 11507
12.3%
0 6177
 
6.6%
2 4572
 
4.9%
8 4090
 
4.4%
9 2405
 
2.6%
4 2175
 
2.3%
Other values (4) 4089
 
4.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 93572
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 20782
22.2%
, 13134
14.0%
13134
14.0%
[ 11507
12.3%
] 11507
12.3%
0 6177
 
6.6%
2 4572
 
4.9%
8 4090
 
4.4%
9 2405
 
2.6%
4 2175
 
2.3%
Other values (4) 4089
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 93572
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 20782
22.2%
, 13134
14.0%
13134
14.0%
[ 11507
12.3%
] 11507
12.3%
0 6177
 
6.6%
2 4572
 
4.9%
8 4090
 
4.4%
9 2405
 
2.6%
4 2175
 
2.3%
Other values (4) 4089
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 93572
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 20782
22.2%
, 13134
14.0%
13134
14.0%
[ 11507
12.3%
] 11507
12.3%
0 6177
 
6.6%
2 4572
 
4.9%
8 4090
 
4.4%
9 2405
 
2.6%
4 2175
 
2.3%
Other values (4) 4089
 
4.4%
Distinct3017
Distinct (%)26.2%
Missing0
Missing (%)0.0%
Memory size90.0 KiB
2024-04-03T14:35:06.718133image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length53
Median length4
Mean length8.471626
Min length3

Characters and Unicode

Total characters97483
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2576 ?
Unique (%)22.4%

Sample

1st row[17, 12, 10]
2nd row[14]
3rd row[13, 18, 20, 13]
4th row[20]
5th row[13]
ValueCountFrequency (%)
12 3451
14.0%
14 3389
13.8%
10 3214
13.0%
16 2874
11.7%
13 2801
11.4%
18 1879
7.6%
11 1715
7.0%
20 1568
6.4%
15 1377
 
5.6%
17 782
 
3.2%
Other values (10) 1591
6.5%
2024-04-03T14:35:06.900049image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 23635
24.2%
, 13134
13.5%
13134
13.5%
[ 11507
11.8%
] 11507
11.8%
2 5168
 
5.3%
0 4782
 
4.9%
4 3406
 
3.5%
6 2909
 
3.0%
3 2803
 
2.9%
Other values (4) 5498
 
5.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 97483
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 23635
24.2%
, 13134
13.5%
13134
13.5%
[ 11507
11.8%
] 11507
11.8%
2 5168
 
5.3%
0 4782
 
4.9%
4 3406
 
3.5%
6 2909
 
3.0%
3 2803
 
2.9%
Other values (4) 5498
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 97483
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 23635
24.2%
, 13134
13.5%
13134
13.5%
[ 11507
11.8%
] 11507
11.8%
2 5168
 
5.3%
0 4782
 
4.9%
4 3406
 
3.5%
6 2909
 
3.0%
3 2803
 
2.9%
Other values (4) 5498
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 97483
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 23635
24.2%
, 13134
13.5%
13134
13.5%
[ 11507
11.8%
] 11507
11.8%
2 5168
 
5.3%
0 4782
 
4.9%
4 3406
 
3.5%
6 2909
 
3.0%
3 2803
 
2.9%
Other values (4) 5498
 
5.6%
Distinct3129
Distinct (%)27.2%
Missing0
Missing (%)0.0%
Memory size90.0 KiB
2024-04-03T14:35:06.986882image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length53
Median length47
Mean length8.227253
Min length3

Characters and Unicode

Total characters94671
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2714 ?
Unique (%)23.6%

Sample

1st row[10, 20, 20]
2nd row[19]
3rd row[14, 9, 13, 20]
4th row[12]
5th row[10]
ValueCountFrequency (%)
10 3189
12.9%
20 2902
11.8%
12 2529
10.3%
14 2524
10.2%
18 2445
9.9%
8 2378
9.7%
13 1721
7.0%
11 1694
6.9%
16 1677
6.8%
9 1112
 
4.5%
Other values (10) 2470
10.0%
2024-04-03T14:35:07.173978image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 19483
20.6%
, 13134
13.9%
13134
13.9%
[ 11507
12.2%
] 11507
12.2%
0 6091
 
6.4%
2 5547
 
5.9%
8 4823
 
5.1%
4 2540
 
2.7%
6 1774
 
1.9%
Other values (4) 5131
 
5.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 94671
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 19483
20.6%
, 13134
13.9%
13134
13.9%
[ 11507
12.2%
] 11507
12.2%
0 6091
 
6.4%
2 5547
 
5.9%
8 4823
 
5.1%
4 2540
 
2.7%
6 1774
 
1.9%
Other values (4) 5131
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 94671
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 19483
20.6%
, 13134
13.9%
13134
13.9%
[ 11507
12.2%
] 11507
12.2%
0 6091
 
6.4%
2 5547
 
5.9%
8 4823
 
5.1%
4 2540
 
2.7%
6 1774
 
1.9%
Other values (4) 5131
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 94671
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 19483
20.6%
, 13134
13.9%
13134
13.9%
[ 11507
12.2%
] 11507
12.2%
0 6091
 
6.4%
2 5547
 
5.9%
8 4823
 
5.1%
4 2540
 
2.7%
6 1774
 
1.9%
Other values (4) 5131
 
5.4%
Distinct5334
Distinct (%)46.4%
Missing0
Missing (%)0.0%
Memory size90.0 KiB
2024-04-03T14:35:07.326634image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length2644
Median length760
Mean length46.424959
Min length6

Characters and Unicode

Total characters534212
Distinct characters142
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4577 ?
Unique (%)39.8%

Sample

1st row['Ghoul', 'Nightveil Specter', 'Ghoul', 'Wraith', 'Mummy', 'Ghoul', 'Boneless', 'Manes', 'Vrock', 'Bearded Devil', 'Yellow Musk Zombie']
2nd row['Giant Rat', 'Giant Rat', 'Giant Rat', 'Giant Rat', 'Giant Rat', 'Swarm of Rats', 'Giant Rat', 'Giant Rat', 'Giant Rat', 'Swarm of Rats', 'Giant Rat', 'Giant Rat', 'Giant Rat', 'Swarm of Rats', 'Swarm of Rats']
3rd row['Adult Red Dragon', 'Aberrant Spirit', 'Frost Giant', 'Frost Giant', 'Bulette', 'Bulette', 'Bulette', 'Young Red Dragon']
4th row['Tooth-N-Claw', 'Skeletal Polar Bear', 'Mimic', 'Tanarukk']
5th row['Mummy Lord']
ValueCountFrequency (%)
giant 2602
 
4.3%
mage 2211
 
3.6%
wolf 1254
 
2.1%
bandit 1167
 
1.9%
rat 1084
 
1.8%
zombie 1024
 
1.7%
goblin 998
 
1.6%
lord 947
 
1.6%
mummy 942
 
1.5%
of 930
 
1.5%
Other values (2189) 47785
78.4%
2024-04-03T14:35:07.690750image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
' 78202
 
14.6%
49439
 
9.3%
e 32182
 
6.0%
a 31886
 
6.0%
r 28219
 
5.3%
, 27754
 
5.2%
o 26006
 
4.9%
i 22315
 
4.2%
n 21996
 
4.1%
t 19339
 
3.6%
Other values (132) 196874
36.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 534212
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
' 78202
 
14.6%
49439
 
9.3%
e 32182
 
6.0%
a 31886
 
6.0%
r 28219
 
5.3%
, 27754
 
5.2%
o 26006
 
4.9%
i 22315
 
4.2%
n 21996
 
4.1%
t 19339
 
3.6%
Other values (132) 196874
36.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 534212
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
' 78202
 
14.6%
49439
 
9.3%
e 32182
 
6.0%
a 31886
 
6.0%
r 28219
 
5.3%
, 27754
 
5.2%
o 26006
 
4.9%
i 22315
 
4.2%
n 21996
 
4.1%
t 19339
 
3.6%
Other values (132) 196874
36.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 534212
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
' 78202
 
14.6%
49439
 
9.3%
e 32182
 
6.0%
a 31886
 
6.0%
r 28219
 
5.3%
, 27754
 
5.2%
o 26006
 
4.9%
i 22315
 
4.2%
n 21996
 
4.1%
t 19339
 
3.6%
Other values (132) 196874
36.9%

monster_number
Real number (ℝ)

HIGH CORRELATION 

Distinct58
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4041019
Minimum1
Maximum194
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size90.0 KiB
2024-04-03T14:35:07.809327image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q34
95-th percentile12
Maximum194
Range193
Interquartile range (IQR)3

Descriptive statistics

Standard deviation5.3889475
Coefficient of variation (CV)1.5830747
Kurtosis198.46876
Mean3.4041019
Median Absolute Deviation (MAD)0
Skewness9.1144201
Sum39171
Variance29.040755
MonotonicityNot monotonic
2024-04-03T14:35:07.912215image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 6093
53.0%
2 1492
 
13.0%
3 859
 
7.5%
4 611
 
5.3%
5 492
 
4.3%
6 399
 
3.5%
7 277
 
2.4%
8 227
 
2.0%
9 172
 
1.5%
10 141
 
1.2%
Other values (48) 744
 
6.5%
ValueCountFrequency (%)
1 6093
53.0%
2 1492
 
13.0%
3 859
 
7.5%
4 611
 
5.3%
5 492
 
4.3%
6 399
 
3.5%
7 277
 
2.4%
8 227
 
2.0%
9 172
 
1.5%
10 141
 
1.2%
ValueCountFrequency (%)
194 1
< 0.1%
118 1
< 0.1%
109 1
< 0.1%
94 1
< 0.1%
87 1
< 0.1%
84 1
< 0.1%
74 1
< 0.1%
67 1
< 0.1%
63 1
< 0.1%
61 2
< 0.1%

monster_total_level
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct467
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.61078
Minimum0
Maximum393.25
Zeros321
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size90.0 KiB
2024-04-03T14:35:08.014429image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.25
Q14
median7
Q315
95-th percentile39.2125
Maximum393.25
Range393.25
Interquartile range (IQR)11

Descriptive statistics

Standard deviation18.019521
Coefficient of variation (CV)1.4288981
Kurtosis75.035568
Mean12.61078
Median Absolute Deviation (MAD)6
Skewness6.3607492
Sum145112.25
Variance324.70313
MonotonicityNot monotonic
2024-04-03T14:35:08.118802image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 1740
 
15.1%
13 890
 
7.7%
15 864
 
7.5%
0.25 490
 
4.3%
2 418
 
3.6%
7 379
 
3.3%
0 321
 
2.8%
9 321
 
2.8%
5 301
 
2.6%
1 296
 
2.6%
Other values (457) 5487
47.7%
ValueCountFrequency (%)
0 321
2.8%
0.125 124
 
1.1%
0.25 490
4.3%
0.375 42
 
0.4%
0.5 214
1.9%
0.625 22
 
0.2%
0.75 82
 
0.7%
0.875 15
 
0.1%
1 296
2.6%
1.125 19
 
0.2%
ValueCountFrequency (%)
393.25 1
< 0.1%
360 1
< 0.1%
322 1
< 0.1%
315.5 1
< 0.1%
293 1
< 0.1%
253 1
< 0.1%
249 1
< 0.1%
234 1
< 0.1%
231.875 1
< 0.1%
225 1
< 0.1%

party_total_level
Real number (ℝ)

HIGH CORRELATION 

Distinct111
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.637177
Minimum0
Maximum136
Zeros8
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size90.0 KiB
2024-04-03T14:35:08.220804image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q17
median12
Q320
95-th percentile46
Maximum136
Range136
Interquartile range (IQR)13

Descriptive statistics

Standard deviation14.261294
Coefficient of variation (CV)0.85719434
Kurtosis6.716465
Mean16.637177
Median Absolute Deviation (MAD)6
Skewness2.1631841
Sum191444
Variance203.38451
MonotonicityNot monotonic
2024-04-03T14:35:08.327736image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 690
 
6.0%
6 664
 
5.8%
7 622
 
5.4%
8 609
 
5.3%
4 588
 
5.1%
10 570
 
5.0%
3 570
 
5.0%
9 488
 
4.2%
12 462
 
4.0%
20 446
 
3.9%
Other values (101) 5798
50.4%
ValueCountFrequency (%)
0 8
 
0.1%
1 35
 
0.3%
2 64
 
0.6%
3 570
5.0%
4 588
5.1%
5 690
6.0%
6 664
5.8%
7 622
5.4%
8 609
5.3%
9 488
4.2%
ValueCountFrequency (%)
136 1
< 0.1%
129 1
< 0.1%
119 1
< 0.1%
118 1
< 0.1%
117 1
< 0.1%
115 1
< 0.1%
114 1
< 0.1%
113 1
< 0.1%
112 1
< 0.1%
109 1
< 0.1%

party_level1_spellslots
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct32
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.7669245
Minimum0
Maximum31
Zeros2780
Zeros (%)24.2%
Negative0
Negative (%)0.0%
Memory size90.0 KiB
2024-04-03T14:35:08.419716image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median4
Q37
95-th percentile14
Maximum31
Range31
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.5212565
Coefficient of variation (CV)0.94846404
Kurtosis2.2487154
Mean4.7669245
Median Absolute Deviation (MAD)3
Skewness1.357297
Sum54853
Variance20.44176
MonotonicityNot monotonic
2024-04-03T14:35:08.505115image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
4 3126
27.2%
0 2780
24.2%
3 877
 
7.6%
8 839
 
7.3%
6 570
 
5.0%
2 472
 
4.1%
7 408
 
3.5%
5 395
 
3.4%
12 335
 
2.9%
10 274
 
2.4%
Other values (22) 1431
12.4%
ValueCountFrequency (%)
0 2780
24.2%
1 244
 
2.1%
2 472
 
4.1%
3 877
 
7.6%
4 3126
27.2%
5 395
 
3.4%
6 570
 
5.0%
7 408
 
3.5%
8 839
 
7.3%
9 202
 
1.8%
ValueCountFrequency (%)
31 1
 
< 0.1%
30 2
 
< 0.1%
29 3
 
< 0.1%
28 1
 
< 0.1%
27 4
 
< 0.1%
26 2
 
< 0.1%
25 3
 
< 0.1%
24 4
 
< 0.1%
23 19
0.2%
22 17
0.1%

party_level2_spellslots
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct22
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8038585
Minimum0
Maximum24
Zeros3943
Zeros (%)34.3%
Negative0
Negative (%)0.0%
Memory size90.0 KiB
2024-04-03T14:35:08.588232image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q33
95-th percentile9
Maximum24
Range24
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.007614
Coefficient of variation (CV)1.0726697
Kurtosis2.8630262
Mean2.8038585
Median Absolute Deviation (MAD)3
Skewness1.4914459
Sum32264
Variance9.0457421
MonotonicityNot monotonic
2024-04-03T14:35:08.667197image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0 3943
34.3%
3 3192
27.7%
2 1327
 
11.5%
6 805
 
7.0%
5 577
 
5.0%
4 281
 
2.4%
8 276
 
2.4%
9 253
 
2.2%
1 249
 
2.2%
7 173
 
1.5%
Other values (12) 431
 
3.7%
ValueCountFrequency (%)
0 3943
34.3%
1 249
 
2.2%
2 1327
 
11.5%
3 3192
27.7%
4 281
 
2.4%
5 577
 
5.0%
6 805
 
7.0%
7 173
 
1.5%
8 276
 
2.4%
9 253
 
2.2%
ValueCountFrequency (%)
24 1
 
< 0.1%
21 1
 
< 0.1%
20 3
 
< 0.1%
18 4
 
< 0.1%
17 9
 
0.1%
16 8
 
0.1%
15 29
 
0.3%
14 43
0.4%
13 30
 
0.3%
12 102
0.9%

party_level3_spellslots
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct18
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7710959
Minimum0
Maximum18
Zeros6082
Zeros (%)52.9%
Negative0
Negative (%)0.0%
Memory size90.0 KiB
2024-04-03T14:35:08.740590image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile6
Maximum18
Range18
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.4151707
Coefficient of variation (CV)1.3636589
Kurtosis3.7570376
Mean1.7710959
Median Absolute Deviation (MAD)0
Skewness1.7296439
Sum20380
Variance5.8330494
MonotonicityNot monotonic
2024-04-03T14:35:08.821103image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0 6082
52.9%
3 2457
21.4%
2 1157
 
10.1%
6 517
 
4.5%
5 358
 
3.1%
4 210
 
1.8%
1 203
 
1.8%
8 160
 
1.4%
9 127
 
1.1%
7 92
 
0.8%
Other values (8) 144
 
1.3%
ValueCountFrequency (%)
0 6082
52.9%
1 203
 
1.8%
2 1157
 
10.1%
3 2457
21.4%
4 210
 
1.8%
5 358
 
3.1%
6 517
 
4.5%
7 92
 
0.8%
8 160
 
1.4%
9 127
 
1.1%
ValueCountFrequency (%)
18 2
 
< 0.1%
16 2
 
< 0.1%
15 7
 
0.1%
14 15
 
0.1%
13 4
 
< 0.1%
12 40
 
0.3%
11 38
 
0.3%
10 36
 
0.3%
9 127
1.1%
8 160
1.4%

party_level4_spellslots
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct16
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.95985053
Minimum0
Maximum15
Zeros7990
Zeros (%)69.4%
Negative0
Negative (%)0.0%
Memory size90.0 KiB
2024-04-03T14:35:08.899852image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile5
Maximum15
Range15
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.7968107
Coefficient of variation (CV)1.8719693
Kurtosis7.4953726
Mean0.95985053
Median Absolute Deviation (MAD)0
Skewness2.4272163
Sum11045
Variance3.2285287
MonotonicityNot monotonic
2024-04-03T14:35:08.976261image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 7990
69.4%
3 1574
 
13.7%
2 623
 
5.4%
1 595
 
5.2%
6 286
 
2.5%
4 136
 
1.2%
5 127
 
1.1%
9 55
 
0.5%
8 44
 
0.4%
7 33
 
0.3%
Other values (6) 44
 
0.4%
ValueCountFrequency (%)
0 7990
69.4%
1 595
 
5.2%
2 623
 
5.4%
3 1574
 
13.7%
4 136
 
1.2%
5 127
 
1.1%
6 286
 
2.5%
7 33
 
0.3%
8 44
 
0.4%
9 55
 
0.5%
ValueCountFrequency (%)
15 3
 
< 0.1%
14 1
 
< 0.1%
13 2
 
< 0.1%
12 14
 
0.1%
11 16
 
0.1%
10 8
 
0.1%
9 55
 
0.5%
8 44
 
0.4%
7 33
 
0.3%
6 286
2.5%

party_level5_spellslots
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.56522117
Minimum0
Maximum15
Zeros8984
Zeros (%)78.1%
Negative0
Negative (%)0.0%
Memory size90.0 KiB
2024-04-03T14:35:09.051543image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum15
Range15
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.2865144
Coefficient of variation (CV)2.2761256
Kurtosis13.495806
Mean0.56522117
Median Absolute Deviation (MAD)0
Skewness3.1013501
Sum6504
Variance1.6551192
MonotonicityNot monotonic
2024-04-03T14:35:09.126626image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 8984
78.1%
2 1139
 
9.9%
3 470
 
4.1%
1 456
 
4.0%
4 223
 
1.9%
5 110
 
1.0%
6 58
 
0.5%
7 25
 
0.2%
8 21
 
0.2%
11 6
 
0.1%
Other values (5) 15
 
0.1%
ValueCountFrequency (%)
0 8984
78.1%
1 456
 
4.0%
2 1139
 
9.9%
3 470
 
4.1%
4 223
 
1.9%
5 110
 
1.0%
6 58
 
0.5%
7 25
 
0.2%
8 21
 
0.2%
9 4
 
< 0.1%
ValueCountFrequency (%)
15 1
 
< 0.1%
14 1
 
< 0.1%
12 3
 
< 0.1%
11 6
 
0.1%
10 6
 
0.1%
9 4
 
< 0.1%
8 21
 
0.2%
7 25
 
0.2%
6 58
0.5%
5 110
1.0%

party_level6_spellslots
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.18049883
Minimum0
Maximum6
Zeros9985
Zeros (%)86.8%
Negative0
Negative (%)0.0%
Memory size90.0 KiB
2024-04-03T14:35:09.200145image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.51907002
Coefficient of variation (CV)2.8757529
Kurtosis15.372793
Mean0.18049883
Median Absolute Deviation (MAD)0
Skewness3.5208322
Sum2077
Variance0.26943368
MonotonicityNot monotonic
2024-04-03T14:35:09.268562image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 9985
86.8%
1 1074
 
9.3%
2 370
 
3.2%
3 56
 
0.5%
4 16
 
0.1%
5 5
 
< 0.1%
6 1
 
< 0.1%
ValueCountFrequency (%)
0 9985
86.8%
1 1074
 
9.3%
2 370
 
3.2%
3 56
 
0.5%
4 16
 
0.1%
5 5
 
< 0.1%
6 1
 
< 0.1%
ValueCountFrequency (%)
6 1
 
< 0.1%
5 5
 
< 0.1%
4 16
 
0.1%
3 56
 
0.5%
2 370
 
3.2%
1 1074
 
9.3%
0 9985
86.8%

party_level7_spellslots
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.13756844
Minimum0
Maximum6
Zeros10320
Zeros (%)89.7%
Negative0
Negative (%)0.0%
Memory size90.0 KiB
2024-04-03T14:35:09.335702image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4562589
Coefficient of variation (CV)3.3165958
Kurtosis25.213402
Mean0.13756844
Median Absolute Deviation (MAD)0
Skewness4.3110764
Sum1583
Variance0.20817219
MonotonicityNot monotonic
2024-04-03T14:35:09.408497image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 10320
89.7%
1 868
 
7.5%
2 272
 
2.4%
3 26
 
0.2%
4 15
 
0.1%
6 3
 
< 0.1%
5 3
 
< 0.1%
ValueCountFrequency (%)
0 10320
89.7%
1 868
 
7.5%
2 272
 
2.4%
3 26
 
0.2%
4 15
 
0.1%
5 3
 
< 0.1%
6 3
 
< 0.1%
ValueCountFrequency (%)
6 3
 
< 0.1%
5 3
 
< 0.1%
4 15
 
0.1%
3 26
 
0.2%
2 272
 
2.4%
1 868
 
7.5%
0 10320
89.7%

party_level8_spellslots
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.086121491
Minimum0
Maximum5
Zeros10624
Zeros (%)92.3%
Negative0
Negative (%)0.0%
Memory size90.0 KiB
2024-04-03T14:35:09.479762image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.31910781
Coefficient of variation (CV)3.7053214
Kurtosis26.925432
Mean0.086121491
Median Absolute Deviation (MAD)0
Skewness4.4699091
Sum991
Variance0.10182979
MonotonicityNot monotonic
2024-04-03T14:35:09.559008image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 10624
92.3%
1 794
 
6.9%
2 75
 
0.7%
3 10
 
0.1%
4 3
 
< 0.1%
5 1
 
< 0.1%
ValueCountFrequency (%)
0 10624
92.3%
1 794
 
6.9%
2 75
 
0.7%
3 10
 
0.1%
4 3
 
< 0.1%
5 1
 
< 0.1%
ValueCountFrequency (%)
5 1
 
< 0.1%
4 3
 
< 0.1%
3 10
 
0.1%
2 75
 
0.7%
1 794
 
6.9%
0 10624
92.3%

party_level9_spellslots
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size90.0 KiB
0
10995 
1
 
474
2
 
30
3
 
5
4
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11507
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 10995
95.6%
1 474
 
4.1%
2 30
 
0.3%
3 5
 
< 0.1%
4 3
 
< 0.1%

Length

2024-04-03T14:35:09.636357image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-03T14:35:09.713724image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 10995
95.6%
1 474
 
4.1%
2 30
 
0.3%
3 5
 
< 0.1%
4 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 10995
95.6%
1 474
 
4.1%
2 30
 
0.3%
3 5
 
< 0.1%
4 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11507
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 10995
95.6%
1 474
 
4.1%
2 30
 
0.3%
3 5
 
< 0.1%
4 3
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11507
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 10995
95.6%
1 474
 
4.1%
2 30
 
0.3%
3 5
 
< 0.1%
4 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11507
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 10995
95.6%
1 474
 
4.1%
2 30
 
0.3%
3 5
 
< 0.1%
4 3
 
< 0.1%

party_total_ac
Real number (ℝ)

HIGH CORRELATION 

Distinct152
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.57817
Minimum9
Maximum240
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size90.0 KiB
2024-04-03T14:35:09.798616image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile13
Q117
median22
Q351
95-th percentile92
Maximum240
Range231
Interquartile range (IQR)34

Descriptive statistics

Standard deviation27.429594
Coefficient of variation (CV)0.74988973
Kurtosis1.5654393
Mean36.57817
Median Absolute Deviation (MAD)8
Skewness1.415649
Sum420905
Variance752.38262
MonotonicityNot monotonic
2024-04-03T14:35:09.899630image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18 978
 
8.5%
17 909
 
7.9%
16 715
 
6.2%
19 682
 
5.9%
15 614
 
5.3%
14 499
 
4.3%
20 461
 
4.0%
33 246
 
2.1%
13 204
 
1.8%
21 201
 
1.7%
Other values (142) 5998
52.1%
ValueCountFrequency (%)
9 7
 
0.1%
10 59
 
0.5%
11 113
 
1.0%
12 198
 
1.7%
13 204
 
1.8%
14 499
4.3%
15 614
5.3%
16 715
6.2%
17 909
7.9%
18 978
8.5%
ValueCountFrequency (%)
240 1
< 0.1%
212 1
< 0.1%
180 1
< 0.1%
175 1
< 0.1%
169 1
< 0.1%
166 1
< 0.1%
162 1
< 0.1%
160 1
< 0.1%
159 1
< 0.1%
158 1
< 0.1%

party_total_precombat_hp
Real number (ℝ)

HIGH CORRELATION 

Distinct674
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean144.98931
Minimum1
Maximum3187
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size90.0 KiB
2024-04-03T14:35:09.996204image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile28
Q158
median104
Q3179
95-th percentile413.7
Maximum3187
Range3186
Interquartile range (IQR)121

Descriptive statistics

Standard deviation139.79785
Coefficient of variation (CV)0.96419417
Kurtosis28.237789
Mean144.98931
Median Absolute Deviation (MAD)54
Skewness3.3588143
Sum1668392
Variance19543.439
MonotonicityNot monotonic
2024-04-03T14:35:10.093325image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38 155
 
1.3%
59 145
 
1.3%
44 144
 
1.3%
31 139
 
1.2%
52 138
 
1.2%
67 122
 
1.1%
28 122
 
1.1%
45 119
 
1.0%
76 118
 
1.0%
51 115
 
1.0%
Other values (664) 10190
88.6%
ValueCountFrequency (%)
1 4
 
< 0.1%
8 6
0.1%
9 4
 
< 0.1%
10 8
0.1%
11 4
 
< 0.1%
12 8
0.1%
13 7
0.1%
14 3
 
< 0.1%
15 2
 
< 0.1%
16 10
0.1%
ValueCountFrequency (%)
3187 1
< 0.1%
1322 1
< 0.1%
1260 1
< 0.1%
1209 1
< 0.1%
1119 1
< 0.1%
1107 1
< 0.1%
1096 1
< 0.1%
1070 1
< 0.1%
1053 1
< 0.1%
1038 1
< 0.1%

party_total_postcombat_hp
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct598
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean113.09681
Minimum0
Maximum1221
Zeros345
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size90.0 KiB
2024-04-03T14:35:10.192091image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9
Q140
median80
Q3143
95-th percentile329
Maximum1221
Range1221
Interquartile range (IQR)103

Descriptive statistics

Standard deviation117.24443
Coefficient of variation (CV)1.0366732
Kurtosis12.703738
Mean113.09681
Median Absolute Deviation (MAD)46
Skewness2.8963248
Sum1301405
Variance13746.257
MonotonicityNot monotonic
2024-04-03T14:35:10.380198image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 345
 
3.0%
38 123
 
1.1%
52 120
 
1.0%
44 115
 
1.0%
45 112
 
1.0%
59 111
 
1.0%
31 109
 
0.9%
28 104
 
0.9%
35 102
 
0.9%
34 102
 
0.9%
Other values (588) 10164
88.3%
ValueCountFrequency (%)
0 345
3.0%
1 36
 
0.3%
2 13
 
0.1%
3 19
 
0.2%
4 37
 
0.3%
5 20
 
0.2%
6 25
 
0.2%
7 35
 
0.3%
8 36
 
0.3%
9 33
 
0.3%
ValueCountFrequency (%)
1221 1
 
< 0.1%
1038 1
 
< 0.1%
999 2
 
< 0.1%
964 5
< 0.1%
957 6
0.1%
945 2
 
< 0.1%
942 1
 
< 0.1%
935 4
< 0.1%
933 2
 
< 0.1%
928 2
 
< 0.1%

party_total_hpratio
Real number (ℝ)

ZEROS 

Distinct4229
Distinct (%)36.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.77665239
Minimum0
Maximum1.5454545
Zeros345
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size90.0 KiB
2024-04-03T14:35:10.474908image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.16808493
Q10.64965942
median0.8742515
Q31
95-th percentile1
Maximum1.5454545
Range1.5454545
Interquartile range (IQR)0.35034058

Descriptive statistics

Standard deviation0.26977963
Coefficient of variation (CV)0.34736213
Kurtosis0.9564515
Mean0.77665239
Median Absolute Deviation (MAD)0.1257485
Skewness-1.3092158
Sum8936.9391
Variance0.072781046
MonotonicityNot monotonic
2024-04-03T14:35:10.575553image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 3745
32.5%
0 345
 
3.0%
0.6666666667 58
 
0.5%
0.75 37
 
0.3%
0.5 37
 
0.3%
0.8333333333 34
 
0.3%
0.875 32
 
0.3%
0.8571428571 26
 
0.2%
0.8 26
 
0.2%
0.7142857143 25
 
0.2%
Other values (4219) 7142
62.1%
ValueCountFrequency (%)
0 345
3.0%
0.004464285714 1
 
< 0.1%
0.004926108374 1
 
< 0.1%
0.005780346821 1
 
< 0.1%
0.006134969325 1
 
< 0.1%
0.007692307692 3
 
< 0.1%
0.007874015748 1
 
< 0.1%
0.01020408163 1
 
< 0.1%
0.01041666667 1
 
< 0.1%
0.01176470588 1
 
< 0.1%
ValueCountFrequency (%)
1.545454545 1
< 0.1%
1.520408163 1
< 0.1%
1.373333333 1
< 0.1%
1.348837209 1
< 0.1%
1.3 1
< 0.1%
1.285714286 1
< 0.1%
1.26984127 1
< 0.1%
1.25 1
< 0.1%
1.222222222 1
< 0.1%
1.1875 1
< 0.1%

party_total_prof_bonus
Real number (ℝ)

HIGH CORRELATION 

Distinct39
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.0901191
Minimum2
Maximum47
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size90.0 KiB
2024-04-03T14:35:10.672478image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q13
median5
Q39
95-th percentile18
Maximum47
Range45
Interquartile range (IQR)6

Descriptive statistics

Standard deviation5.3616113
Coefficient of variation (CV)0.75620892
Kurtosis3.5254898
Mean7.0901191
Median Absolute Deviation (MAD)2
Skewness1.7338432
Sum81586
Variance28.746875
MonotonicityNot monotonic
2024-04-03T14:35:10.760518image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
3 2100
18.2%
6 1494
13.0%
4 1461
12.7%
5 1230
10.7%
2 1197
10.4%
8 566
 
4.9%
12 527
 
4.6%
10 469
 
4.1%
15 376
 
3.3%
9 350
 
3.0%
Other values (29) 1737
15.1%
ValueCountFrequency (%)
2 1197
10.4%
3 2100
18.2%
4 1461
12.7%
5 1230
10.7%
6 1494
13.0%
7 315
 
2.7%
8 566
 
4.9%
9 350
 
3.0%
10 469
 
4.1%
11 168
 
1.5%
ValueCountFrequency (%)
47 1
 
< 0.1%
42 2
 
< 0.1%
40 2
 
< 0.1%
38 1
 
< 0.1%
36 5
< 0.1%
35 4
< 0.1%
34 1
 
< 0.1%
33 2
 
< 0.1%
32 2
 
< 0.1%
31 7
0.1%

party_total_strength
Real number (ℝ)

HIGH CORRELATION 

Distinct130
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.404884
Minimum3
Maximum175
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size90.0 KiB
2024-04-03T14:35:10.849880image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile8
Q111
median18
Q338
95-th percentile73
Maximum175
Range172
Interquartile range (IQR)27

Descriptive statistics

Standard deviation21.723114
Coefficient of variation (CV)0.79267308
Kurtosis2.0813023
Mean27.404884
Median Absolute Deviation (MAD)9
Skewness1.4946576
Sum315348
Variance471.89368
MonotonicityNot monotonic
2024-04-03T14:35:10.948793image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8 1453
 
12.6%
18 834
 
7.2%
10 815
 
7.1%
16 575
 
5.0%
20 547
 
4.8%
13 380
 
3.3%
19 376
 
3.3%
12 361
 
3.1%
11 337
 
2.9%
9 303
 
2.6%
Other values (120) 5526
48.0%
ValueCountFrequency (%)
3 4
 
< 0.1%
4 4
 
< 0.1%
5 3
 
< 0.1%
6 22
 
0.2%
7 39
 
0.3%
8 1453
12.6%
9 303
 
2.6%
10 815
7.1%
11 337
 
2.9%
12 361
 
3.1%
ValueCountFrequency (%)
175 1
< 0.1%
162 1
< 0.1%
158 1
< 0.1%
152 1
< 0.1%
150 1
< 0.1%
142 1
< 0.1%
137 1
< 0.1%
134 1
< 0.1%
131 2
< 0.1%
129 2
< 0.1%

party_total_dexterity
Real number (ℝ)

HIGH CORRELATION 

Distinct141
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.582863
Minimum5
Maximum208
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size90.0 KiB
2024-04-03T14:35:11.049946image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile10
Q114
median20
Q346
95-th percentile83
Maximum208
Range203
Interquartile range (IQR)32

Descriptive statistics

Standard deviation24.860182
Coefficient of variation (CV)0.76298337
Kurtosis1.6391184
Mean32.582863
Median Absolute Deviation (MAD)8
Skewness1.420208
Sum374931
Variance618.02866
MonotonicityNot monotonic
2024-04-03T14:35:11.153324image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14 1415
 
12.3%
16 941
 
8.2%
20 746
 
6.5%
18 718
 
6.2%
10 437
 
3.8%
12 395
 
3.4%
17 377
 
3.3%
13 277
 
2.4%
19 255
 
2.2%
32 211
 
1.8%
Other values (131) 5735
49.8%
ValueCountFrequency (%)
5 2
 
< 0.1%
6 63
 
0.5%
7 4
 
< 0.1%
8 115
 
1.0%
9 47
 
0.4%
10 437
 
3.8%
11 195
 
1.7%
12 395
 
3.4%
13 277
 
2.4%
14 1415
12.3%
ValueCountFrequency (%)
208 1
< 0.1%
186 1
< 0.1%
175 1
< 0.1%
164 1
< 0.1%
157 1
< 0.1%
156 2
< 0.1%
150 2
< 0.1%
144 1
< 0.1%
140 2
< 0.1%
139 1
< 0.1%

party_total_constitution
Real number (ℝ)

HIGH CORRELATION 

Distinct142
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.999131
Minimum4
Maximum198
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size90.0 KiB
2024-04-03T14:35:11.263925image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile13
Q115
median19
Q346
95-th percentile84
Maximum198
Range194
Interquartile range (IQR)31

Descriptive statistics

Standard deviation25.073305
Coefficient of variation (CV)0.75981712
Kurtosis1.5128124
Mean32.999131
Median Absolute Deviation (MAD)7
Skewness1.4153515
Sum379721
Variance628.67061
MonotonicityNot monotonic
2024-04-03T14:35:11.370742image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14 1777
 
15.4%
16 1315
 
11.4%
18 601
 
5.2%
15 599
 
5.2%
13 432
 
3.8%
17 408
 
3.5%
12 384
 
3.3%
20 383
 
3.3%
28 291
 
2.5%
30 279
 
2.4%
Other values (132) 5038
43.8%
ValueCountFrequency (%)
4 5
 
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
9 4
 
< 0.1%
10 109
 
0.9%
11 61
 
0.5%
12 384
 
3.3%
13 432
 
3.8%
14 1777
15.4%
15 599
 
5.2%
ValueCountFrequency (%)
198 1
< 0.1%
192 1
< 0.1%
180 1
< 0.1%
171 1
< 0.1%
155 2
< 0.1%
154 1
< 0.1%
153 1
< 0.1%
152 1
< 0.1%
148 1
< 0.1%
144 1
< 0.1%

party_total_intelligence
Real number (ℝ)

HIGH CORRELATION 

Distinct118
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.266533
Minimum5
Maximum163
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size90.0 KiB
2024-04-03T14:35:11.470763image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile8
Q110
median18
Q337
95-th percentile70
Maximum163
Range158
Interquartile range (IQR)27

Descriptive statistics

Standard deviation21.26193
Coefficient of variation (CV)0.80946844
Kurtosis1.7076809
Mean26.266533
Median Absolute Deviation (MAD)9
Skewness1.4403995
Sum302249
Variance452.06966
MonotonicityNot monotonic
2024-04-03T14:35:11.569760image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8 1443
 
12.5%
10 1229
 
10.7%
14 597
 
5.2%
11 589
 
5.1%
12 560
 
4.9%
20 488
 
4.2%
9 473
 
4.1%
18 439
 
3.8%
19 279
 
2.4%
16 263
 
2.3%
Other values (108) 5147
44.7%
ValueCountFrequency (%)
5 4
 
< 0.1%
6 3
 
< 0.1%
7 12
 
0.1%
8 1443
12.5%
9 473
 
4.1%
10 1229
10.7%
11 589
5.1%
12 560
 
4.9%
13 209
 
1.8%
14 597
5.2%
ValueCountFrequency (%)
163 1
 
< 0.1%
157 1
 
< 0.1%
155 1
 
< 0.1%
136 2
< 0.1%
135 2
< 0.1%
133 1
 
< 0.1%
127 1
 
< 0.1%
126 1
 
< 0.1%
120 2
< 0.1%
117 4
< 0.1%

party_total_wisdom
Real number (ℝ)

HIGH CORRELATION 

Distinct133
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.556357
Minimum4
Maximum175
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size90.0 KiB
2024-04-03T14:35:11.669002image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile10
Q113
median18
Q341.5
95-th percentile76
Maximum175
Range171
Interquartile range (IQR)28.5

Descriptive statistics

Standard deviation22.882328
Coefficient of variation (CV)0.7741931
Kurtosis1.6278423
Mean29.556357
Median Absolute Deviation (MAD)8
Skewness1.4125227
Sum340105
Variance523.60092
MonotonicityNot monotonic
2024-04-03T14:35:11.770528image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 1160
 
10.1%
12 903
 
7.8%
16 822
 
7.1%
14 764
 
6.6%
13 607
 
5.3%
18 441
 
3.8%
15 379
 
3.3%
20 376
 
3.3%
11 281
 
2.4%
26 249
 
2.2%
Other values (123) 5525
48.0%
ValueCountFrequency (%)
4 2
 
< 0.1%
7 5
 
< 0.1%
8 237
 
2.1%
9 100
 
0.9%
10 1160
10.1%
11 281
 
2.4%
12 903
7.8%
13 607
5.3%
14 764
6.6%
15 379
 
3.3%
ValueCountFrequency (%)
175 1
< 0.1%
167 1
< 0.1%
163 2
< 0.1%
152 1
< 0.1%
149 1
< 0.1%
148 1
< 0.1%
144 1
< 0.1%
143 1
< 0.1%
141 1
< 0.1%
134 1
< 0.1%

party_total_charisma
Real number (ℝ)

HIGH CORRELATION 

Distinct132
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.00504
Minimum3
Maximum175
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size90.0 KiB
2024-04-03T14:35:11.868379image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile8
Q112
median20
Q340
95-th percentile77
Maximum175
Range172
Interquartile range (IQR)28

Descriptive statistics

Standard deviation23.118239
Coefficient of variation (CV)0.79704211
Kurtosis1.6410253
Mean29.00504
Median Absolute Deviation (MAD)10
Skewness1.4293448
Sum333761
Variance534.45296
MonotonicityNot monotonic
2024-04-03T14:35:11.965598image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8 1075
 
9.3%
10 977
 
8.5%
20 833
 
7.2%
18 645
 
5.6%
14 567
 
4.9%
12 521
 
4.5%
13 434
 
3.8%
16 375
 
3.3%
11 320
 
2.8%
15 208
 
1.8%
Other values (122) 5552
48.2%
ValueCountFrequency (%)
3 3
 
< 0.1%
4 1
 
< 0.1%
5 3
 
< 0.1%
6 21
 
0.2%
7 24
 
0.2%
8 1075
9.3%
9 182
 
1.6%
10 977
8.5%
11 320
 
2.8%
12 521
4.5%
ValueCountFrequency (%)
175 1
< 0.1%
165 1
< 0.1%
162 1
< 0.1%
154 1
< 0.1%
152 1
< 0.1%
150 1
< 0.1%
144 1
< 0.1%
143 1
< 0.1%
136 1
< 0.1%
132 1
< 0.1%

player_monster_ratio
Real number (ℝ)

HIGH CORRELATION 

Distinct168
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0863177
Minimum0.014925373
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size90.0 KiB
2024-04-03T14:35:12.065303image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.014925373
5-th percentile0.16666667
Q10.5
median1
Q31
95-th percentile3
Maximum9
Range8.9850746
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.87122731
Coefficient of variation (CV)0.80200046
Kurtosis9.9034525
Mean1.0863177
Median Absolute Deviation (MAD)0.33333333
Skewness2.6179832
Sum12500.258
Variance0.75903702
MonotonicityNot monotonic
2024-04-03T14:35:12.159144image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 5223
45.4%
2 1108
 
9.6%
0.5 1001
 
8.7%
0.3333333333 464
 
4.0%
3 256
 
2.2%
0.6666666667 256
 
2.2%
0.25 215
 
1.9%
1.5 180
 
1.6%
0.2 163
 
1.4%
4 159
 
1.4%
Other values (158) 2482
21.6%
ValueCountFrequency (%)
0.01492537313 1
< 0.1%
0.01546391753 1
< 0.1%
0.01587301587 1
< 0.1%
0.01639344262 1
< 0.1%
0.01886792453 1
< 0.1%
0.02173913043 1
< 0.1%
0.02631578947 1
< 0.1%
0.02702702703 1
< 0.1%
0.02752293578 1
< 0.1%
0.02777777778 1
< 0.1%
ValueCountFrequency (%)
9 1
 
< 0.1%
8 3
 
< 0.1%
7 5
 
< 0.1%
6 39
 
0.3%
5 119
1.0%
4.5 2
 
< 0.1%
4 159
1.4%
3.5 7
 
0.1%
3 256
2.2%
2.666666667 3
 
< 0.1%

monster_player_ratio
Real number (ℝ)

HIGH CORRELATION 

Distinct168
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8390525
Minimum0.11111111
Maximum67
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size90.0 KiB
2024-04-03T14:35:12.249420image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.11111111
5-th percentile0.33333333
Q11
median1
Q32
95-th percentile6
Maximum67
Range66.888889
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.8968978
Coefficient of variation (CV)1.5752122
Kurtosis114.14762
Mean1.8390525
Median Absolute Deviation (MAD)0.33333333
Skewness8.1936931
Sum21161.977
Variance8.3920169
MonotonicityNot monotonic
2024-04-03T14:35:12.348032image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 5223
45.4%
0.5 1108
 
9.6%
2 1001
 
8.7%
3 464
 
4.0%
0.3333333333 256
 
2.2%
1.5 256
 
2.2%
4 215
 
1.9%
0.6666666667 180
 
1.6%
5 163
 
1.4%
0.25 159
 
1.4%
Other values (158) 2482
21.6%
ValueCountFrequency (%)
0.1111111111 1
 
< 0.1%
0.125 3
 
< 0.1%
0.1428571429 5
 
< 0.1%
0.1666666667 39
 
0.3%
0.2 119
1.0%
0.2222222222 2
 
< 0.1%
0.25 159
1.4%
0.2857142857 7
 
0.1%
0.3333333333 256
2.2%
0.375 3
 
< 0.1%
ValueCountFrequency (%)
67 1
< 0.1%
64.66666667 1
< 0.1%
63 1
< 0.1%
61 1
< 0.1%
53 1
< 0.1%
46 1
< 0.1%
38 1
< 0.1%
37 1
< 0.1%
36.33333333 1
< 0.1%
36 1
< 0.1%

Interactions

2024-04-03T14:34:58.155960image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:04.887185image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:06.944957image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:09.031674image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:11.130046image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:13.224625image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:15.261467image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:17.325133image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:19.275427image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:21.299396image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:23.608529image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:25.588963image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:27.695265image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:29.644831image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:31.695923image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:33.663372image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:35.665929image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:37.647803image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:39.722292image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:41.872425image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:43.768890image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:45.881110image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:47.906448image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:50.023204image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:52.175247image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:54.205761image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:56.296180image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:58.226516image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:04.964269image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:07.018422image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:09.099336image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:11.201318image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:13.295096image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:15.329777image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:17.391405image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:19.434728image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:21.431321image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:23.678090image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:25.657826image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:27.764324image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:29.710265image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:31.763345image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:33.727263image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:35.735012image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:37.803676image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:39.796417image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:41.938204image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:43.838100image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:45.950401image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:47.978734image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:50.093196image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:52.245350image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:54.273891image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:56.361862image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:58.306034image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:05.041438image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:07.098425image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:09.179074image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:11.280558image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
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2024-04-03T14:34:23.389765image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:25.374504image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:27.472410image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:29.432535image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:31.480693image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:33.439346image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:35.461742image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:37.430618image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:39.508216image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:41.647222image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:43.565285image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:45.664553image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:47.688084image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:49.804048image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:51.949937image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:53.987908image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:56.079085image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:57.955355image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:35:00.086567image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:06.811275image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:08.885333image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:10.989657image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:13.082362image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:15.115564image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:17.184216image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:19.135293image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:21.149303image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:23.464343image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:25.448597image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:27.545714image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:29.506068image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:31.553970image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:33.522893image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:35.531375image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:37.505075image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:39.582804image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:41.727674image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:43.636457image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:45.739260image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:47.761687image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:49.880128image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:52.031259image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:54.064189image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:56.152307image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:58.026988image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:35:00.152843image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:06.875598image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:08.953949image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:11.057556image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:13.149498image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:15.183669image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:17.250006image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:19.204237image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:21.212110image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:23.533189image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:25.514452image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:27.622604image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:29.570881image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:31.618275image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:33.588315image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:35.595790image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:37.569651image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:39.648802image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:41.795041image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:43.699273image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:45.805596image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:47.831777image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:49.946933image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:52.100791image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:54.131051image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:56.218719image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T14:34:58.086508image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Correlations

2024-04-03T14:35:12.440116image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Unnamed: 0monster_numbermonster_player_ratiomonster_total_levelparty_level1_spellslotsparty_level2_spellslotsparty_level3_spellslotsparty_level4_spellslotsparty_level5_spellslotsparty_level6_spellslotsparty_level7_spellslotsparty_level8_spellslotsparty_level9_spellslotsparty_sizeparty_total_acparty_total_charismaparty_total_constitutionparty_total_dexterityparty_total_hpratioparty_total_intelligenceparty_total_levelparty_total_postcombat_hpparty_total_precombat_hpparty_total_prof_bonusparty_total_strengthparty_total_wisdomplayer_monster_ratiostart_time
Unnamed: 01.0000.003-0.001-0.0010.0140.0150.0100.0120.0040.0010.0040.0040.0000.0070.0100.0060.0030.0140.0090.0060.0020.000-0.0010.0050.0020.0150.0010.004
monster_number0.0031.0000.6780.2530.3470.2900.1930.0930.0420.008-0.023-0.0270.0000.5100.4970.4970.5120.477-0.2760.5140.3380.2150.3410.4350.5000.497-0.6780.028
monster_player_ratio-0.0010.6781.0000.112-0.144-0.109-0.054-0.032-0.006-0.017-0.019-0.0130.000-0.214-0.154-0.148-0.143-0.169-0.128-0.125-0.119-0.153-0.104-0.160-0.129-0.152-1.0000.050
monster_total_level-0.0010.2530.1121.0000.2740.3210.3870.3740.3410.2810.2620.2330.1630.2260.2790.1960.2430.206-0.0740.1840.5830.4580.5670.4850.1840.196-0.112-0.032
party_level1_spellslots0.0140.347-0.1440.2741.0000.8600.6320.4380.2930.2520.2120.1760.1160.6790.6400.7130.6350.566-0.0770.6390.5830.4670.5220.6660.5680.6280.144-0.024
party_level2_spellslots0.0150.290-0.1090.3210.8601.0000.7570.5430.3820.3270.2740.2250.1500.5550.5330.6050.5500.447-0.0490.5490.6020.4850.5340.6150.4530.5220.109-0.014
party_level3_spellslots0.0100.193-0.0540.3870.6320.7571.0000.7450.5530.4680.4000.3370.2480.3510.3660.4290.3860.285-0.0000.3810.6030.4940.5320.5450.2670.3420.0540.002
party_level4_spellslots0.0120.093-0.0320.3740.4380.5430.7451.0000.7820.6560.5710.4840.3580.1830.2250.2640.2390.1510.0480.2350.5370.4540.4770.4350.1290.1940.0320.010
party_level5_spellslots0.0040.042-0.0060.3410.2930.3820.5530.7821.0000.7620.6700.5770.4340.0750.1270.1710.1440.0610.0790.1400.4750.4110.4230.3700.0260.0950.0060.021
party_level6_spellslots0.0010.008-0.0170.2810.2520.3270.4680.6560.7621.0000.8330.7120.5510.0410.0940.1090.1120.0390.0910.1170.3930.3480.3510.2990.0020.0820.0170.007
party_level7_spellslots0.004-0.023-0.0190.2620.2120.2740.4000.5710.6700.8331.0000.8390.5580.0010.0610.0750.0760.0070.1090.0660.3510.3200.3160.264-0.0370.0450.0190.003
party_level8_spellslots0.004-0.027-0.0130.2330.1760.2250.3370.4840.5770.7120.8391.0000.687-0.0120.0440.0620.058-0.0040.0950.0470.3100.2800.2810.229-0.0440.0260.013-0.003
party_level9_spellslots0.0000.0000.0000.1630.1160.1500.2480.3580.4340.5510.5580.6871.000-0.0330.0240.0210.031-0.0240.0770.0250.2350.2070.2110.170-0.0570.008-0.0190.027
party_size0.0070.510-0.2140.2260.6790.5550.3510.1830.0750.0410.001-0.012-0.0331.0000.8940.8840.8960.892-0.2180.8800.6330.4960.6100.8220.8690.8950.214-0.041
party_total_ac0.0100.497-0.1540.2790.6400.5330.3660.2250.1270.0940.0610.0440.0240.8941.0000.8220.8760.837-0.1690.7980.7130.5920.7130.8540.8720.8630.154-0.011
party_total_charisma0.0060.497-0.1480.1960.7130.6050.4290.2640.1710.1090.0750.0620.0210.8840.8221.0000.8230.788-0.1950.7990.6170.4740.5760.7800.7900.7730.148-0.040
party_total_constitution0.0030.512-0.1430.2430.6350.5500.3860.2390.1440.1120.0760.0580.0310.8960.8760.8231.0000.803-0.2210.8080.6590.5580.6910.8150.8600.8330.143-0.023
party_total_dexterity0.0140.477-0.1690.2060.5660.4470.2850.1510.0610.0390.007-0.004-0.0240.8920.8370.7880.8031.000-0.1800.8610.6260.4810.5860.7900.7470.8700.169-0.007
party_total_hpratio0.009-0.276-0.128-0.074-0.077-0.049-0.0000.0480.0790.0910.1090.0950.077-0.218-0.169-0.195-0.221-0.1801.000-0.200-0.0280.339-0.038-0.095-0.227-0.2050.128-0.010
party_total_intelligence0.0060.514-0.1250.1840.6390.5490.3810.2350.1400.1170.0660.0470.0250.8800.7980.7990.8080.861-0.2001.0000.6050.4450.5520.7710.7490.8380.125-0.011
party_total_level0.0020.338-0.1190.5830.5830.6020.6030.5370.4750.3930.3510.3100.2350.6330.7130.6170.6590.626-0.0280.6051.0000.8390.9540.9350.5910.6040.1190.010
party_total_postcombat_hp0.0000.215-0.1530.4580.4670.4850.4940.4540.4110.3480.3200.2800.2070.4960.5920.4740.5580.4810.3390.4450.8391.0000.8770.7750.4920.4640.1530.012
party_total_precombat_hp-0.0010.341-0.1040.5670.5220.5340.5320.4770.4230.3510.3160.2810.2110.6100.7130.5760.6910.586-0.0380.5520.9540.8771.0000.8980.6140.5780.1040.012
party_total_prof_bonus0.0050.435-0.1600.4850.6660.6150.5450.4350.3700.2990.2640.2290.1700.8220.8540.7800.8150.790-0.0950.7710.9350.7750.8981.0000.7510.7740.160-0.004
party_total_strength0.0020.500-0.1290.1840.5680.4530.2670.1290.0260.002-0.037-0.044-0.0570.8690.8720.7900.8600.747-0.2270.7490.5910.4920.6140.7511.0000.7880.129-0.032
party_total_wisdom0.0150.497-0.1520.1960.6280.5220.3420.1940.0950.0820.0450.0260.0080.8950.8630.7730.8330.870-0.2050.8380.6040.4640.5780.7740.7881.0000.152-0.031
player_monster_ratio0.001-0.678-1.000-0.1120.1440.1090.0540.0320.0060.0170.0190.013-0.0190.2140.1540.1480.1430.1690.1280.1250.1190.1530.1040.1600.1290.1521.000-0.050
start_time0.0040.0280.050-0.032-0.024-0.0140.0020.0100.0210.0070.003-0.0030.027-0.041-0.011-0.040-0.023-0.007-0.010-0.0110.0100.0120.012-0.004-0.032-0.031-0.0501.000

Missing values

2024-04-03T14:35:00.316984image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-03T14:35:00.771654image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Unnamed: 0combat_idstart_timeplayer_idsplayer_infomonsters_infoparty_sizetotal_slotstotal_max_slotsparty_classes_with_levelparty_total_class_compositionplayer_individual_hp_ratiosplayer_individual_acplayer_individual_prof_bonusplayer_individual_strengthplayer_individual_dexterityplayer_individual_constitutionplayer_individual_intelligenceplayer_individual_wisdomplayer_individual_charismamonster_typesmonster_numbermonster_total_levelparty_total_levelparty_level1_spellslotsparty_level2_spellslotsparty_level3_spellslotsparty_level4_spellslotsparty_level5_spellslotsparty_level6_spellslotsparty_level7_spellslotsparty_level8_spellslotsparty_level9_spellslotsparty_total_acparty_total_precombat_hpparty_total_postcombat_hpparty_total_hpratioparty_total_prof_bonusparty_total_strengthparty_total_dexterityparty_total_constitutionparty_total_intelligenceparty_total_wisdomparty_total_charismaplayer_monster_ratiomonster_player_ratio
011663969629-c5cb4fec-4e5a-4e34-8fd3-4b86d729dd801.663970e+09['198813640578542210', '276755891880615063', '753313111965843135'][{'hp_ratio': (147, 147), 'class': [('Fighter', 13)], 'slots': {'1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'max_slots': {'1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'ac': 19, 'stats': {'prof_bonus': 5, 'strength': 20, 'dexterity': 12, 'constitution': 20, 'intelligence': 10, 'wisdom': 17, 'charisma': 10}}, {'hp_ratio': None, 'class': [('Paladin', 7), ('Fighter', 1)], 'slots': {'1': 4, '2': 3, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'max_slots': {'1': 4, '2': 3, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'ac': 19, 'stats': {'prof_bonus': 3, 'strength': 19, 'dexterity': 10, 'constitution': 16, 'intelligence': 9, 'wisdom': 12, 'charisma': 20}}, {'hp_ratio': (20, 58), 'class': [('Paladin', 6)], 'slots': {'1': 4, '2': 2, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'max_slots': {'1': 4, '2': 2, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'ac': 11, 'stats': {'prof_bonus': 3, 'strength': 16, 'dexterity': 12, 'constitution': 16, 'intelligence': 10, 'wisdom': 10, 'charisma': 20}}][{'monster_id': 'af27cde4-5fca-42ed-a676-3ffec286601c', 'monster_code': 'GH1', 'monster_name': 'Ghoul', 'level': 1.0}, {'monster_id': '5f73b515-afca-48fa-9b87-3f53d718cc57', 'monster_code': 'NS1', 'monster_name': 'Nightveil Specter', 'level': 10.0}, {'monster_id': 'b9b9e516-a047-4db0-922c-080060c76383', 'monster_code': 'GH1', 'monster_name': 'Ghoul', 'level': 1.0}, {'monster_id': '64f8d6c2-8c1a-4896-a0e4-7fb2f4f1f526', 'monster_code': 'WR1', 'monster_name': 'Wraith', 'level': 5.0}, {'monster_id': 'abfed520-dd1e-4d1c-af63-d0818be5d09f', 'monster_code': 'MU1', 'monster_name': 'Mummy', 'level': 3.0}, {'monster_id': 'b62e450b-07dc-4e02-a5b0-6543b4ec02a9', 'monster_code': 'GH1', 'monster_name': 'Ghoul', 'level': 1.0}, {'monster_id': '76092654-606a-4cff-9160-4944ae1c1780', 'monster_code': 'BO1', 'monster_name': 'Boneless', 'level': 1.0}, {'monster_id': '2ef9d2a7-3751-48c5-b4e5-8b2a36e9c55a', 'monster_code': 'MA1', 'monster_name': 'Manes', 'level': 0.125}, {'monster_id': '6d1d38e8-b118-4bb7-8cfe-7c356d55f519', 'monster_code': 'VR1', 'monster_name': 'Vrock', 'level': 6.0}, {'monster_id': '70f6ee1e-ab05-4879-af89-3df127b696d3', 'monster_code': 'BD1', 'monster_name': 'Bearded Devil', 'level': 3.0}, {'monster_id': '4cfa2697-e6cf-4b89-b4aa-98dbc66f93da', 'monster_code': 'YMZ1', 'monster_name': 'Yellow Musk Zombie', 'level': 0.25}]3{'1': 8, '2': 5, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}{'1': 8, '2': 5, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}[('Fighter', 13), ('Paladin', 7), ('Fighter', 1), ('Paladin', 6)]['Fighter', 'Paladin', 'Fighter', 'Paladin'][(147, 147), (20, 58)][19, 19, 11][5, 3, 3][20, 19, 16][12, 10, 12][20, 16, 16][10, 9, 10][17, 12, 10][10, 20, 20]['Ghoul', 'Nightveil Specter', 'Ghoul', 'Wraith', 'Mummy', 'Ghoul', 'Boneless', 'Manes', 'Vrock', 'Bearded Devil', 'Yellow Musk Zombie']1131.37527850000000492051670.814634115534522939500.2727273.666667
171667093704-73130db4-c26b-4778-a242-0b98c46adabc1.667094e+09['177558997848408361'][{'hp_ratio': (0, 23), 'class': [('Warlock', 4)], 'slots': {'1': 0, '2': 2, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'max_slots': {'1': 0, '2': 2, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'ac': 19, 'stats': {'prof_bonus': 2, 'strength': 20, 'dexterity': 9, 'constitution': 10, 'intelligence': 12, 'wisdom': 14, 'charisma': 19}}][{'monster_id': '4c7d90bb-9255-44a9-aa4e-f6ffa1fa9000', 'monster_code': 'GR1', 'monster_name': 'Giant Rat', 'level': 0.125}, {'monster_id': 'b5491d16-ca83-4996-82d1-41d1ec1b7db6', 'monster_code': 'GR2', 'monster_name': 'Giant Rat', 'level': 0.125}, {'monster_id': '6ede1067-ed54-42bb-9ce2-14068477164c', 'monster_code': 'GR3', 'monster_name': 'Giant Rat', 'level': 0.125}, {'monster_id': '15406585-e135-4bde-b13a-95e9039052c4', 'monster_code': 'GR1', 'monster_name': 'Giant Rat', 'level': 0.125}, {'monster_id': 'cba094e4-cbda-4cb3-b96d-6783a378cba9', 'monster_code': 'GR3', 'monster_name': 'Giant Rat', 'level': 0.125}, {'monster_id': '535a9ef0-3c3c-4719-824c-761ee6ba0675', 'monster_code': 'SoR1', 'monster_name': 'Swarm of Rats', 'level': 0.25}, {'monster_id': '18ae3599-3f5f-434c-abf4-08985400956c', 'monster_code': 'GR1', 'monster_name': 'Giant Rat', 'level': 0.125}, {'monster_id': '5b665adc-16fb-4a81-93da-3476c692d470', 'monster_code': 'GR2', 'monster_name': 'Giant Rat', 'level': 0.125}, {'monster_id': '5369b5d1-45e9-4242-8a9b-1d4280822a1e', 'monster_code': 'GR3', 'monster_name': 'Giant Rat', 'level': 0.125}, {'monster_id': '5a49cbdf-9890-4826-bb02-98a9f60cf367', 'monster_code': 'SoR1', 'monster_name': 'Swarm of Rats', 'level': 0.25}, {'monster_id': 'f752f71e-0840-4f4c-af8e-73cd169acf8a', 'monster_code': 'GR1', 'monster_name': 'Giant Rat', 'level': 0.125}, {'monster_id': 'ff186863-f2ab-40f0-b194-8e2f4965b444', 'monster_code': 'GR2', 'monster_name': 'Giant Rat', 'level': 0.125}, {'monster_id': '62baee43-b320-414a-b2fb-1010a1635bf0', 'monster_code': 'GR3', 'monster_name': 'Giant Rat', 'level': 0.125}, {'monster_id': '0162fa37-9f5a-4be5-b820-437ef078ae4e', 'monster_code': 'SoR1', 'monster_name': 'Swarm of Rats', 'level': 0.25}, {'monster_id': 'd1084b11-b70b-4b0b-826d-ef22e6237863', 'monster_code': 'SoR2', 'monster_name': 'Swarm of Rats', 'level': 0.25}]1{'1': 0, '2': 2, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}{'1': 0, '2': 2, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}[('Warlock', 4)]['Warlock'][(0, 23)][19][2][20][9][10][12][14][19]['Giant Rat', 'Giant Rat', 'Giant Rat', 'Giant Rat', 'Giant Rat', 'Swarm of Rats', 'Giant Rat', 'Giant Rat', 'Giant Rat', 'Swarm of Rats', 'Giant Rat', 'Giant Rat', 'Giant Rat', 'Swarm of Rats', 'Swarm of Rats']152.3754020000000192300.0000002209101214190.06666715.000000
2101662316101-ff0b283d-fbfe-497c-b139-2f0c0a48f0bd1.662316e+09['196558367709637569', '263880573407494191', '278328381340542754', '583320224080630228'][{'hp_ratio': (133, 238), 'class': [('Fighter', 18)], 'slots': {'1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'max_slots': {'1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'ac': 19, 'stats': {'prof_bonus': 6, 'strength': 20, 'dexterity': 8, 'constitution': 20, 'intelligence': 9, 'wisdom': 13, 'charisma': 14}}, {'hp_ratio': (116, 116), 'class': [('Blood Hunter', 16)], 'slots': {'1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'max_slots': {'1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'ac': 17, 'stats': {'prof_bonus': 5, 'strength': 11, 'dexterity': 20, 'constitution': 12, 'intelligence': 11, 'wisdom': 18, 'charisma': 9}}, {'hp_ratio': (117, 155), 'class': [('Monk', 13), ('Cleric', 6)], 'slots': {'1': 4, '2': 3, '3': 3, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'max_slots': {'1': 4, '2': 3, '3': 3, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'ac': 21, 'stats': {'prof_bonus': 6, 'strength': 10, 'dexterity': 20, 'constitution': 16, 'intelligence': 9, 'wisdom': 20, 'charisma': 13}}, {'hp_ratio': None, 'class': [('Sorcerer', 16), ('Bard', 3)], 'slots': {'1': 4, '2': 3, '3': 3, '4': 3, '5': 3, '6': 2, '7': 1, '8': 1, '9': 1}, 'max_slots': {'1': 4, '2': 3, '3': 3, '4': 3, '5': 3, '6': 2, '7': 1, '8': 1, '9': 1}, 'ac': 19, 'stats': {'prof_bonus': 6, 'strength': 14, 'dexterity': 10, 'constitution': 20, 'intelligence': 8, 'wisdom': 13, 'charisma': 20}}][{'monster_id': '5b6213ac-cff6-416c-a8f0-784730069478', 'monster_code': 'ARD1', 'monster_name': 'Adult Red Dragon', 'level': 17.0}, {'monster_id': '27bec593-43b4-45b5-894c-59e88426f0e6', 'monster_code': 'Galileo', 'monster_name': 'Aberrant Spirit', 'level': 0.125}, {'monster_id': '9ab5e823-ad13-410d-900d-be441f25fdf4', 'monster_code': 'FG2', 'monster_name': 'Frost Giant', 'level': 8.0}, {'monster_id': '41241090-6ed8-4b07-a8a3-3b9acde4d593', 'monster_code': 'FG1', 'monster_name': 'Frost Giant', 'level': 8.0}, {'monster_id': '9d8e5263-4270-449b-8286-72980342d5ee', 'monster_code': 'BU1', 'monster_name': 'Bulette', 'level': 5.0}, {'monster_id': 'a2f438da-b6c0-4ed0-b1f3-73954c416080', 'monster_code': 'BU2', 'monster_name': 'Bulette', 'level': 5.0}, {'monster_id': 'b6c2dfcf-f5d4-47f4-a9fe-5063a5609d97', 'monster_code': 'BU3', 'monster_name': 'Bulette', 'level': 5.0}, {'monster_id': '467c09bd-9a77-472a-a881-4694e963f20a', 'monster_code': 'YRD1', 'monster_name': 'Young Red Dragon', 'level': 10.0}]4{'1': 8, '2': 6, '3': 6, '4': 3, '5': 3, '6': 2, '7': 1, '8': 1, '9': 1}{'1': 8, '2': 6, '3': 6, '4': 3, '5': 3, '6': 2, '7': 1, '8': 1, '9': 1}[('Fighter', 18), ('Blood Hunter', 16), ('Monk', 13), ('Cleric', 6), ('Sorcerer', 16), ('Bard', 3)]['Fighter', 'Blood Hunter', 'Monk', 'Cleric', 'Sorcerer', 'Bard'][(133, 238), (116, 116), (117, 155)][19, 17, 21, 19][6, 5, 6, 6][20, 11, 10, 14][8, 20, 20, 10][20, 12, 16, 20][9, 11, 9, 8][13, 18, 20, 13][14, 9, 13, 20]['Adult Red Dragon', 'Aberrant Spirit', 'Frost Giant', 'Frost Giant', 'Bulette', 'Bulette', 'Bulette', 'Young Red Dragon']858.12572866332111765093660.719057235558683764560.5000002.000000
3121665031867-37c024ab-8ea8-4a95-8f27-7bed6426c3831.665032e+09['645474316434980766'][{'hp_ratio': (107, 107), 'class': [('Druid', 13)], 'slots': {'1': 4, '2': 3, '3': 3, '4': 3, '5': 2, '6': 1, '7': 1, '8': 0, '9': 0}, 'max_slots': {'1': 4, '2': 3, '3': 3, '4': 3, '5': 2, '6': 1, '7': 1, '8': 0, '9': 0}, 'ac': 19, 'stats': {'prof_bonus': 5, 'strength': 10, 'dexterity': 16, 'constitution': 16, 'intelligence': 10, 'wisdom': 20, 'charisma': 12}}][{'monster_id': 'b2cd27b1-6e9e-42cb-bd32-9babc3c733eb', 'monster_code': 'TNC1', 'monster_name': 'Tooth-N-Claw', 'level': 3.0}, {'monster_id': '82c5d818-6226-43fd-967f-caa4cce78f2b', 'monster_code': 'SPB1', 'monster_name': 'Skeletal Polar Bear', 'level': 2.0}, {'monster_id': '4c203c45-e419-4e01-bfd8-d7dbe6b220cb', 'monster_code': 'MI1', 'monster_name': 'Mimic', 'level': 2.0}, {'monster_id': '7b68f331-3930-40f6-9ea2-199df1b2ce4b', 'monster_code': 'TA1', 'monster_name': 'Tanarukk', 'level': 5.0}]1{'1': 4, '2': 3, '3': 3, '4': 3, '5': 2, '6': 1, '7': 1, '8': 0, '9': 0}{'1': 4, '2': 3, '3': 3, '4': 3, '5': 2, '6': 1, '7': 1, '8': 0, '9': 0}[('Druid', 13)]['Druid'][(107, 107)][19][5][10][16][16][10][20][12]['Tooth-N-Claw', 'Skeletal Polar Bear', 'Mimic', 'Tanarukk']412.00013433321100191071071.00000051016161020120.2500004.000000
4131661703086-0acbaf03-f0dc-4884-bcfd-08bc33f681911.661703e+09['423192139884408071'][{'hp_ratio': (147, 147), 'class': [('Fighter', 13)], 'slots': {'1': 0, '2': 1, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'max_slots': {'1': 0, '2': 1, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'ac': 22, 'stats': {'prof_bonus': 5, 'strength': 20, 'dexterity': 8, 'constitution': 20, 'intelligence': 12, 'wisdom': 13, 'charisma': 10}}][{'monster_id': '7c3aef0b-1606-46a7-92c9-5a43e46ab5ba', 'monster_code': 'ML1', 'monster_name': 'Mummy Lord', 'level': 15.0}]1{'1': 0, '2': 1, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}{'1': 0, '2': 1, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}[('Fighter', 13)]['Fighter'][(147, 147)][22][5][20][8][20][12][13][10]['Mummy Lord']115.00013010000000221471471.0000005208201213101.0000001.000000
5151668745287-8002d22f-a396-4bf1-9a64-e3e8e3688fe91.668745e+09['462847866580788120', '172773523848272123', '227650240967229361', '194210523128447213'][{'hp_ratio': (53, 76), 'class': [('Fighter', 8)], 'slots': {'1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'max_slots': {'1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'ac': 21, 'stats': {'prof_bonus': 3, 'strength': 19, 'dexterity': 18, 'constitution': 17, 'intelligence': 12, 'wisdom': 12, 'charisma': 10}}, {'hp_ratio': (53, 53), 'class': [('Sorcerer', 9), ('Cleric', 1)], 'slots': {'1': 4, '2': 2, '3': 2, '4': 3, '5': 1, '6': 0, '7': 0, '8': 0, '9': 0}, 'max_slots': {'1': 4, '2': 3, '3': 3, '4': 3, '5': 2, '6': 0, '7': 0, '8': 0, '9': 0}, 'ac': 18, 'stats': {'prof_bonus': 4, 'strength': 9, 'dexterity': 16, 'constitution': 12, 'intelligence': 11, 'wisdom': 18, 'charisma': 20}}, {'hp_ratio': (85, 96), 'class': [('Paladin', 6), ('Sorcerer', 4)], 'slots': {'1': 1, '2': 2, '3': 2, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'max_slots': {'1': 4, '2': 3, '3': 3, '4': 1, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'ac': 22, 'stats': {'prof_bonus': 4, 'strength': 18, 'dexterity': 12, 'constitution': 19, 'intelligence': 9, 'wisdom': 10, 'charisma': 20}}, {'hp_ratio': (19, 21), 'class': [('Paladin', 2), ('Warlock', 1)], 'slots': {'1': 2, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'max_slots': {'1': 3, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'ac': 18, 'stats': {'prof_bonus': 2, 'strength': 13, 'dexterity': 16, 'constitution': 10, 'intelligence': 9, 'wisdom': 14, 'charisma': 20}}][{'monster_id': 'e36ab35d-ab3d-4687-8309-f73087d3e958', 'monster_code': 'GH2', 'monster_name': 'Ghoul', 'level': 1.0}, {'monster_id': 'df6af532-7377-4b10-b2de-f7e70187bbeb', 'monster_code': 'GH1', 'monster_name': 'Ghoul', 'level': 1.0}, {'monster_id': '70cd28f5-0139-4ed1-8df1-39d1725fd52c', 'monster_code': 'M1', 'monster_name': '\u200e Maurezhi', 'level': 7.0}, {'monster_id': '27b1520a-a2dd-46f3-8df6-0635d17cb227', 'monster_code': 'ZC1', 'monster_name': 'Zombie Clot', 'level': 6.0}]4{'1': 7, '2': 4, '3': 4, '4': 3, '5': 1, '6': 0, '7': 0, '8': 0, '9': 0}{'1': 11, '2': 6, '3': 6, '4': 4, '5': 2, '6': 0, '7': 0, '8': 0, '9': 0}[('Fighter', 8), ('Sorcerer', 9), ('Cleric', 1), ('Paladin', 6), ('Sorcerer', 4), ('Paladin', 2), ('Warlock', 1)]['Fighter', 'Sorcerer', 'Cleric', 'Paladin', 'Sorcerer', 'Paladin', 'Warlock'][(53, 76), (53, 53), (85, 96), (19, 21)][21, 18, 22, 18][3, 4, 4, 2][19, 9, 18, 13][18, 16, 12, 16][17, 12, 19, 10][12, 11, 9, 9][12, 18, 10, 14][10, 20, 20, 20]['Ghoul', 'Ghoul', '\u200e Maurezhi', 'Zombie Clot']415.00031744310000792462100.853659135962584154701.0000001.000000
6181669430529-d8660dd7-c937-4943-b368-a7038bc84b4e1.669431e+09['270844290287396869'][{'hp_ratio': (183, 183), 'class': [('Druid', 20)], 'slots': {'1': 4, '2': 3, '3': 3, '4': 3, '5': 3, '6': 2, '7': 2, '8': 1, '9': 1}, 'max_slots': {'1': 4, '2': 3, '3': 3, '4': 3, '5': 3, '6': 2, '7': 2, '8': 1, '9': 1}, 'ac': 22, 'stats': {'prof_bonus': 6, 'strength': 12, 'dexterity': 14, 'constitution': 18, 'intelligence': 13, 'wisdom': 22, 'charisma': 13}}][{'monster_id': 'a78127f9-3425-40b0-baa3-e98d96232ac0', 'monster_code': 'ASD1', 'monster_name': 'Adult Silver Dragon', 'level': 16.0}]1{'1': 4, '2': 3, '3': 3, '4': 3, '5': 3, '6': 2, '7': 2, '8': 1, '9': 1}{'1': 4, '2': 3, '3': 3, '4': 3, '5': 3, '6': 2, '7': 2, '8': 1, '9': 1}[('Druid', 20)]['Druid'][(183, 183)][22][6][12][14][18][13][22][13]['Adult Silver Dragon']116.00020433332211221831831.00000061214181322131.0000001.000000
7191667705666-7304b590-ace6-45e1-bb6c-ff784927d5151.667706e+09['747367536164297677', '138155110007350782'][{'hp_ratio': (14, 18), 'class': [('Barbarian', 1), ('Fighter', 1)], 'slots': {'1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'max_slots': {'1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'ac': 11, 'stats': {'prof_bonus': 2, 'strength': 15, 'dexterity': 12, 'constitution': 10, 'intelligence': 13, 'wisdom': 8, 'charisma': 16}}, {'hp_ratio': (28, 28), 'class': [('Fighter', 3)], 'slots': {'1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'max_slots': {'1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'ac': 18, 'stats': {'prof_bonus': 2, 'strength': 16, 'dexterity': 14, 'constitution': 15, 'intelligence': 12, 'wisdom': 14, 'charisma': 10}}][{'monster_id': '1f895afc-653d-42de-ba3a-76a97a160898', 'monster_code': 'GO1', 'monster_name': 'Gray Ooze', 'level': 0.5}]2{'1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}{'1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}[('Barbarian', 1), ('Fighter', 1), ('Fighter', 3)]['Barbarian', 'Fighter', 'Fighter'][(14, 18), (28, 28)][11, 18][2, 2][15, 16][12, 14][10, 15][13, 12][8, 14][16, 10]['Gray Ooze']10.50050000000002946420.91304343126252522262.0000000.500000
8271658885575-ee4d97ac-453a-4de1-ac3d-a10dc227bb921.658886e+09['127965999109502658'][{'hp_ratio': (0, 75), 'class': [('Bard', 8)], 'slots': {'1': 4, '2': 1, '3': 3, '4': 2, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'max_slots': {'1': 4, '2': 3, '3': 3, '4': 2, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'ac': 19, 'stats': {'prof_bonus': 3, 'strength': 19, 'dexterity': 7, 'constitution': 18, 'intelligence': 15, 'wisdom': 15, 'charisma': 17}}][{'monster_id': 'c20a1c98-e82c-4906-893d-0a9ebb2ba3ef', 'monster_code': 'HO1', 'monster_name': 'Half-Ogre', 'level': 1.0}, {'monster_id': 'e074b563-a6be-4b87-8532-ae0b2782bbb9', 'monster_code': 'HO2', 'monster_name': 'Half-Ogre', 'level': 1.0}, {'monster_id': '6d0a08d1-c8f0-445d-a510-a36e343710d8', 'monster_code': 'HO3', 'monster_name': 'Half-Ogre', 'level': 1.0}, {'monster_id': '1b3b4139-47a2-45eb-a3cf-a3fd0f2f9bfa', 'monster_code': 'HO4', 'monster_name': 'Half-Ogre', 'level': 1.0}, {'monster_id': 'cdcfd1ca-1dcc-460d-a2e3-374badb50c35', 'monster_code': 'HO5', 'monster_name': 'Half-Ogre', 'level': 1.0}, {'monster_id': '5561f699-49ad-4b38-83f4-c7cd88548c74', 'monster_code': 'HO6', 'monster_name': 'Half-Ogre', 'level': 1.0}]1{'1': 4, '2': 1, '3': 3, '4': 2, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}{'1': 4, '2': 3, '3': 3, '4': 2, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}[('Bard', 8)]['Bard'][(0, 75)][19][3][19][7][18][15][15][17]['Half-Ogre', 'Half-Ogre', 'Half-Ogre', 'Half-Ogre', 'Half-Ogre', 'Half-Ogre']66.0008413200000197500.0000003197181515170.1666676.000000
9291660048742-c8156212-115a-4532-b8b8-172bb9af25561.660049e+09['264055759207672591'][{'hp_ratio': (84, 84), 'class': [('Barbarian', 7), ('Fighter', 1)], 'slots': {'1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'max_slots': {'1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'ac': 15, 'stats': {'prof_bonus': 3, 'strength': 17, 'dexterity': 14, 'constitution': 16, 'intelligence': 8, 'wisdom': 12, 'charisma': 8}}][{'monster_id': 'b891122f-b2bb-49db-8469-2de22f89069c', 'monster_code': 'DM1', 'monster_name': 'Drow Mage', 'level': 7.0}]1{'1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}{'1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}[('Barbarian', 7), ('Fighter', 1)]['Barbarian', 'Fighter'][(84, 84)][15][3][17][14][16][8][12][8]['Drow Mage']17.00080000000001584841.000000317141681281.0000001.000000
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11497247251666693626-0c960fed-6968-4f0d-922f-decedb03d4a11.666694e+09['483720663430960073'][{'hp_ratio': (65, 65), 'class': [('Barbarian', 5)], 'slots': {'1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'max_slots': {'1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'ac': 17, 'stats': {'prof_bonus': 3, 'strength': 18, 'dexterity': 14, 'constitution': 20, 'intelligence': 9, 'wisdom': 12, 'charisma': 10}}][{'monster_id': '85295475-8742-4f59-9370-6351534792ab', 'monster_code': 'WO1', 'monster_name': 'Wolf', 'level': 0.25}]1{'1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}{'1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}[('Barbarian', 5)]['Barbarian'][(65, 65)][17][3][18][14][20][9][12][10]['Wolf']10.2550000000001765651.0000003181420912101.0000001.00
11498247281663856173-de204b7d-4e9b-4988-9969-f9e0c9e7448f1.663856e+09['264055759207672591'][{'hp_ratio': (104, 104), 'class': [('Sorcerer', 17)], 'slots': {'1': 4, '2': 3, '3': 3, '4': 3, '5': 2, '6': 1, '7': 1, '8': 1, '9': 1}, 'max_slots': {'1': 4, '2': 3, '3': 3, '4': 3, '5': 2, '6': 1, '7': 1, '8': 1, '9': 1}, 'ac': 14, 'stats': {'prof_bonus': 6, 'strength': 8, 'dexterity': 14, 'constitution': 15, 'intelligence': 10, 'wisdom': 12, 'charisma': 22}}][{'monster_id': 'a6488197-bc4b-4b97-afb2-f1673849c83a', 'monster_code': 'ML1', 'monster_name': 'Mummy Lord', 'level': 15.0}]1{'1': 4, '2': 3, '3': 3, '4': 3, '5': 2, '6': 1, '7': 1, '8': 1, '9': 1}{'1': 4, '2': 3, '3': 3, '4': 3, '5': 2, '6': 1, '7': 1, '8': 1, '9': 1}[('Sorcerer', 17)]['Sorcerer'][(104, 104)][14][6][8][14][15][10][12][22]['Mummy Lord']115.0017433321111141041041.0000006814151012221.0000001.00
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11500247311665913947-11c5c5e3-cb01-4aa2-9ad2-a7b2402d18391.665914e+09['387476072085517002', '299624821276559746', '171178298810781935', '294037842287691462'][{'hp_ratio': (89, 89), 'class': [('Druid', 4), ('Fighter', 5)], 'slots': {'1': 4, '2': 3, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'max_slots': {'1': 4, '2': 3, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'ac': 18, 'stats': {'prof_bonus': 4, 'strength': 20, 'dexterity': 14, 'constitution': 18, 'intelligence': 10, 'wisdom': 14, 'charisma': 10}}, {'hp_ratio': (136, 136), 'class': [('Paladin', 11)], 'slots': {'1': 4, '2': 3, '3': 3, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'max_slots': {'1': 4, '2': 3, '3': 3, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'ac': 18, 'stats': {'prof_bonus': 4, 'strength': 14, 'dexterity': 6, 'constitution': 19, 'intelligence': 10, 'wisdom': 14, 'charisma': 20}}, {'hp_ratio': (103, 103), 'class': [('Paladin', 9)], 'slots': {'1': 4, '2': 3, '3': 2, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'max_slots': {'1': 4, '2': 3, '3': 2, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'ac': 16, 'stats': {'prof_bonus': 4, 'strength': 6, 'dexterity': 14, 'constitution': 17, 'intelligence': 14, 'wisdom': 14, 'charisma': 20}}, {'hp_ratio': (113, 113), 'class': [('Barbarian', 9)], 'slots': {'1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'max_slots': {'1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'ac': 17, 'stats': {'prof_bonus': 4, 'strength': 18, 'dexterity': 15, 'constitution': 20, 'intelligence': 5, 'wisdom': 12, 'charisma': 12}}][{'monster_id': 'e1343380-b38d-4bcf-9586-ff13e9da9d4e', 'monster_code': 'WE1', 'monster_name': 'Werebear', 'level': 5.0}, {'monster_id': 'd1ce467d-338f-431f-bde1-b88fb4172498', 'monster_code': 'DingoSnaps', 'monster_name': 'Dire Wolf', 'level': 1.0}, {'monster_id': '0b7e34fb-49bb-4f6e-8497-7b83dd6b64fd', 'monster_code': 'TB1', 'monster_name': 'Tree Blight', 'level': 7.0}, {'monster_id': '498a26a4-3e34-4892-af68-847fff973e91', 'monster_code': 'DD2', 'monster_name': 'Death Dog', 'level': 1.0}, {'monster_id': '41e9e436-1f27-4b4c-bd88-1952949f7183', 'monster_code': 'DD1', 'monster_name': 'Death Dog', 'level': 1.0}]4{'1': 12, '2': 9, '3': 5, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}{'1': 12, '2': 9, '3': 5, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}[('Druid', 4), ('Fighter', 5), ('Paladin', 11), ('Paladin', 9), ('Barbarian', 9)]['Druid', 'Fighter', 'Paladin', 'Paladin', 'Barbarian'][(89, 89), (136, 136), (103, 103), (113, 113)][18, 18, 16, 17][4, 4, 4, 4][20, 14, 6, 18][14, 6, 14, 15][18, 19, 17, 20][10, 10, 14, 5][14, 14, 14, 12][10, 20, 20, 12]['Werebear', 'Dire Wolf', 'Tree Blight', 'Death Dog', 'Death Dog']515.00381295000000694414411.000000165849743954620.8000001.25
11501247321658880890-dae21e06-b8d4-4171-ae03-aa1d6dad99091.658881e+09['210939402750190034', '135977390364768782', '100524134196010098', '255701072600205226', '246089410167888447'][{'hp_ratio': (3, 38), 'class': [('Sorcerer', 6)], 'slots': {'1': 4, '2': 3, '3': 3, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'max_slots': {'1': 4, '2': 3, '3': 3, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'ac': 12, 'stats': {'prof_bonus': 3, 'strength': 8, 'dexterity': 14, 'constitution': 14, 'intelligence': 10, 'wisdom': 12, 'charisma': 18}}, {'hp_ratio': (49, 49), 'class': [('Fighter', 5)], 'slots': {'1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'max_slots': {'1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'ac': 16, 'stats': {'prof_bonus': 3, 'strength': 16, 'dexterity': 14, 'constitution': 16, 'intelligence': 10, 'wisdom': 10, 'charisma': 8}}, {'hp_ratio': (2, 28), 'class': [('Druid', 5)], 'slots': {'1': 4, '2': 3, '3': 2, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'max_slots': {'1': 4, '2': 3, '3': 2, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'ac': 12, 'stats': {'prof_bonus': 3, 'strength': 8, 'dexterity': 8, 'constitution': 10, 'intelligence': 16, 'wisdom': 18, 'charisma': 14}}, {'hp_ratio': (36, 59), 'class': [('Rogue', 7), ('Warlock', 1)], 'slots': {'1': 5, '2': 2, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'max_slots': {'1': 5, '2': 2, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'ac': 17, 'stats': {'prof_bonus': 3, 'strength': 8, 'dexterity': 18, 'constitution': 14, 'intelligence': 12, 'wisdom': 12, 'charisma': 14}}, {'hp_ratio': (7, 38), 'class': [('Rogue', 5)], 'slots': {'1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'max_slots': {'1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'ac': 15, 'stats': {'prof_bonus': 3, 'strength': 12, 'dexterity': 18, 'constitution': 14, 'intelligence': 8, 'wisdom': 14, 'charisma': 12}}][{'monster_id': '1c6f923c-0a39-4af8-bf11-726f91b6d1ec', 'monster_code': 'HGS1', 'monster_name': 'Hill Giant Sergeant', 'level': 5.0}, {'monster_id': 'f216d6c6-7c33-49b2-851b-4a4bdc05f85b', 'monster_code': 'MAA1', 'monster_name': 'Martial Arts Adept', 'level': 3.0}, {'monster_id': 'c3cf6477-b132-4462-affa-75a71d306f2b', 'monster_code': 'Escar (Giant Ape)', 'monster_name': 'Giant Ape', 'level': 7.0}, {'monster_id': '1ed296d1-8aa5-4564-8414-970b9bb4751f', 'monster_code': 'DW1', 'monster_name': 'Dire Wolf', 'level': 1.0}]5{'1': 13, '2': 8, '3': 5, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}{'1': 13, '2': 8, '3': 5, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}[('Sorcerer', 6), ('Fighter', 5), ('Druid', 5), ('Rogue', 7), ('Warlock', 1), ('Rogue', 5)]['Sorcerer', 'Fighter', 'Druid', 'Rogue', 'Warlock', 'Rogue'][(3, 38), (49, 49), (2, 28), (36, 59), (7, 38)][12, 16, 12, 17, 15][3, 3, 3, 3, 3][8, 16, 8, 8, 12][14, 14, 8, 18, 18][14, 16, 10, 14, 14][10, 10, 16, 12, 8][12, 10, 18, 12, 14][18, 8, 14, 14, 12]['Hill Giant Sergeant', 'Martial Arts Adept', 'Giant Ape', 'Dire Wolf']416.0029138500000072212970.457547155272685666661.2500000.80
11502247391665954162-bc6d6ca5-04f4-4e62-a17e-1c501d89263a1.665954e+09['327601077009165698', '583320224080630228'][{'hp_ratio': (16, 71), 'class': [('Cleric', 6), ('Paladin', 2)], 'slots': {'1': 4, '2': 3, '3': 3, '4': 1, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'max_slots': {'1': 4, '2': 3, '3': 3, '4': 1, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'ac': 23, 'stats': {'prof_bonus': 3, 'strength': 16, 'dexterity': 9, 'constitution': 14, 'intelligence': 12, 'wisdom': 18, 'charisma': 14}}, {'hp_ratio': (29, 93), 'class': [('Sorcerer', 6), ('Warlock', 3)], 'slots': {'1': 4, '2': 5, '3': 3, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'max_slots': {'1': 4, '2': 5, '3': 3, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'ac': 16, 'stats': {'prof_bonus': 4, 'strength': 16, 'dexterity': 7, 'constitution': 18, 'intelligence': 11, 'wisdom': 13, 'charisma': 18}}][{'monster_id': 'f3142b1a-bf34-4a60-a3f1-c793cbcc98e8', 'monster_code': 'LO1', 'monster_name': 'Lorthuun', 'level': 9.0}, {'monster_id': '953eb497-6d6f-47ac-84e3-71310313e5b8', 'monster_code': 'BH1', 'monster_name': 'Bheur Hag', 'level': 7.0}, {'monster_id': '7bf856bf-e66f-4934-af36-e2e6fa37713e', 'monster_code': 'WE1', 'monster_name': 'Werewolf', 'level': 3.0}, {'monster_id': '059764b7-aae9-4e83-8870-188eb5a6c817', 'monster_code': 'WE3', 'monster_name': 'Werewolf', 'level': 3.0}, {'monster_id': '4a8d1ba2-071f-408c-acf6-a864eb3151cd', 'monster_code': 'WE2', 'monster_name': 'Werewolf', 'level': 3.0}, {'monster_id': 'b1f55445-9a20-45ac-9d71-8565f68837fc', 'monster_code': 'BH2', 'monster_name': 'Bheur Hag', 'level': 7.0}]2{'1': 8, '2': 8, '3': 6, '4': 1, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}{'1': 8, '2': 8, '3': 6, '4': 1, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}[('Cleric', 6), ('Paladin', 2), ('Sorcerer', 6), ('Warlock', 3)]['Cleric', 'Paladin', 'Sorcerer', 'Warlock'][(16, 71), (29, 93)][23, 16][3, 4][16, 16][9, 7][14, 18][12, 11][18, 13][14, 18]['Lorthuun', 'Bheur Hag', 'Werewolf', 'Werewolf', 'Werewolf', 'Bheur Hag']632.001788610000039164450.27439073216322331320.3333333.00
11503247411659572808-af3fa737-e136-4a40-8ea9-e2261a8370281.659573e+09['192437459265741711'][{'hp_ratio': (35, 66), 'class': [('Druid', 9)], 'slots': {'1': 4, '2': 3, '3': 3, '4': 2, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'max_slots': {'1': 4, '2': 3, '3': 3, '4': 3, '5': 1, '6': 0, '7': 0, '8': 0, '9': 0}, 'ac': 14, 'stats': {'prof_bonus': 4, 'strength': 8, 'dexterity': 15, 'constitution': 15, 'intelligence': 12, 'wisdom': 17, 'charisma': 10}}][{'monster_id': 'c3a425bc-3c88-4ecc-bde4-bb39089ba81b', 'monster_code': 'DM1', 'monster_name': 'Drow Mage', 'level': 7.0}, {'monster_id': '1f8d01bd-d42d-49b7-985f-c9e891f1b278', 'monster_code': 'DM2', 'monster_name': 'Drow Mage', 'level': 7.0}]1{'1': 4, '2': 3, '3': 3, '4': 2, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}{'1': 4, '2': 3, '3': 3, '4': 3, '5': 1, '6': 0, '7': 0, '8': 0, '9': 0}[('Druid', 9)]['Druid'][(35, 66)][14][4][8][15][15][12][17][10]['Drow Mage', 'Drow Mage']214.0094332000001466350.5303034815151217100.5000002.00
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11505247441663407358-31b3ea2e-a322-4099-a9af-2203f91016f61.663407e+09['240184232026852789'][{'hp_ratio': (1, 1), 'class': [('Warlock', 5)], 'slots': {'1': 0, '2': 0, '3': 2, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'max_slots': {'1': 0, '2': 0, '3': 2, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'ac': 14, 'stats': {'prof_bonus': 3, 'strength': 10, 'dexterity': 16, 'constitution': 4, 'intelligence': 12, 'wisdom': 13, 'charisma': 16}}][{'monster_id': 'b398e3f8-9cc9-418f-82b0-302868a86974', 'monster_code': 'EN1', 'monster_name': 'Enchanter', 'level': 5.0}]1{'1': 0, '2': 0, '3': 2, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}{'1': 0, '2': 0, '3': 2, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}[('Warlock', 5)]['Warlock'][(1, 1)][14][3][10][16][4][12][13][16]['Enchanter']15.00500200000014111.0000003101641213161.0000001.00
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